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Who Do We Fire When the Agent Gets It Wrong? Inside the Agentic AI SOC

By Jeremy Hehl
Agents can triage, enrich and correlate at machine speed. But someone still has to own the risk call. On Episode 18 of EC-Council’s The Cybersecurity Podcast, Foresite Cybersecurity’s Jeremy Hehl tells host Jay Bavisi why the fully autonomous SOC is further off than vendors suggest, and why governance is the next real frontier.

Every security operations center now claims to be “AI-powered.” For MSSPs, saying anything else risks being labeled old school. So what does an agentic AI SOC actually look like in production?

Jay Bavisi, founder of EC-Council and host of The Cybersecurity Podcast, put that question to Jeremy Hehl, Chief Cybersecurity Evangelist at Foresite Cybersecurity, where he leads the company’s Google SecOps partnership. Their 40-minute conversation covered the limits of autonomy, the future of the SIEM, the fate of L1 analysts, ISO 42001, and the post-quantum risk that keeps Hehl awake at night.

Agents compute, humans decide

Hehl sees real promise in autonomous security operations. Investigation, triage, data aggregation, enrichment and correlation can already be handled by agents “pretty seamlessly,” he said. The hurdle is risk determination: deciding whether something actually matters to the business.

“The question I always ask customers who come to us and say that they want a fully autonomous SOC is: who do we fire when something goes wrong? Do we just dismiss the agent?”

Foresite’s approach treats agents as force multipliers rather than replacements. Hehl compared it to handing analysts a tricorder, a tool that scans and characterizes a threat instantly so the human can decide what to do. Bavisi summed up the model as “agents compute, human decides,” and Hehl agreed, adding that the risk call is the deciding factor.

Is the SIEM on borrowed time?

Asked why Foresite’s Google work matters when most premier SIEMs already ship with AI built in, Hehl pointed to a coming paradigm shift in where data lives. Data sovereignty, regulated industries and ever-growing telemetry volumes are straining the traditional model.

Today, a SIEM aggregates logs into a central plane, normalizes them into fields, and runs detection logic over the result. Hehl expects that to change. Logs will increasingly stay where they reside, in a hyperscaler such as Google, Azure or AWS, and agents will synthesize the raw telemetry in place.

“The idea of a SIEM may become something that is deprecated pretty rapidly.”

He likened the economics to cell phones in the 1990s, when minutes and text messages became commoditized. Data storage will become an assumption, and providers will bill for agent activity and “token burn” instead. Keeping telemetry in BigQuery, he noted, is far cheaper than keeping it hot in a separate SIEM. “The technology has to support it, and we’re not quite there yet,” he added.

The platform is also open by design. Foresite builds primarily on Google’s Vertex AI and Gemini, but Hehl said almost any model, including Claude, can be wired in through APIs and credentials. Agents are, in effect, privileged users, though wiring in other models takes real engineering, parsing and upkeep.

Is the SIEM on borrowed time?

Bavisi raised a trend seen worldwide: L1 roles vanishing, then L2, leaving only L3. Is that simply where agentic technology is heading?

Hehl said Foresite’s experience runs the other way. “We haven’t eliminated jobs. We’ve only grown,” he said. Adding agents to triage, threat hunting and analysis has let analysts handle 10 to 15 times the workload. They dismiss false positives quickly and spend the time saved on learning and more meaningful work.

He invoked the Industrial Revolution in England and France, when coal-powered textile machinery drew a Luddite backlash and even riots. Over time, workers using the machines became far more productive, and business owners demanded more output. This is the pattern known as Jevons Paradox, and Hehl sees agentic AI in the SOC following the same path.

Having started his own career red-teaming banks and working in a SOC, Hehl knows the burnout of triaging the same alert queue “over and over and over again.” Agents that correlate alerts into single cases, surface heuristic patterns and flag statistical outliers do that work “really, really well,” he said. The drudgery goes away; the analyst does not.

The few vendors promising fully autonomous SOC operations, he added, are narrow and tightly focused. Agents that reach a decision and enforce an action must be rigorously tested and simulated, and without clean data they hallucinate. Removing human control over security posture “is pretty unlikely in the next decade or so.”

Taming a non-deterministic technology

Bavisi pressed on a core engineering problem. Ask a frontier model the same question five times and you may get five different answers. How do you build a SOC on that without hallucinations and with consistent results?

Hehl’s answer: a measured, staged deployment. Start with narrow, targeted use cases where machines clearly beat humans, such as data manipulation, API access, running queries and writing code. Then build up capability and maturity step by step.

In enterprise engagements, Foresite first gets data normalization to “basically four nines.” Only then do agents go into the automation playbooks. Hehl argued that calling an agent today is not much different from calling a cloud function that once sat as Python inside a SOAR playbook.

“If you set up markdowns and guidelines and files and restrict the identity of the agent to only do very prescriptive things, as opposed to being all-encompassing, those are the outcomes that customers are looking for.”

He also described three types of MSSP customer. Some just want their brand and stakeholders protected. Some want an extension of their blue team bench. Others come to learn the “massive wall of knobs and levers” in Google’s security portfolio. Mid-market and SMB clients mostly treat agentic capability as part of the service. Enterprises usually already run agents elsewhere in the business and need a careful path to using them for security.

Governance: the next frontier for MSSPs

The industry has swung from “fire all humans and buy all the tokens” to “fire all the tokens and hire all the humans,” Bavisi observed, and is now wobbling in the middle. Where does AI governance fit for customers?

Hehl called it “the next real frontier” for managed security providers, with ISO/IEC 42001 emerging as the benchmark in the US. As public sector bodies adopt hyperscale platforms with agentic capabilities built in, they will need observability into what fleets of agents with permissive access are doing and saying to each other. That raises hard questions: where the harness sits, what protocols apply, and how to read intent from prompts and agent behavior.

“There’s a difference between an agent that sits there and does a prescriptive task … and when we start allowing agents to quantify risk and call other tools.”

He cited Wiz, which maps every model and agent in an environment and the lines of communication between them, and partner Safe AI for observability of agent activity. The CISO’s real dilemma, he said, is simple: “I’m being told I have to deploy AI. I am deploying AI. How do I secure this?”

The forgotten middle

Bavisi divided the world into three tiers. The top 5%, such as Google, Salesforce and Microsoft, have ISO 42001, red teams and consultants. At the other end are organizations that think two ChatGPT subscriptions make them an AI company. The vast majority sit in the middle. They know AI is more than chatbots but don’t know how to adopt, defend or govern it, and they cannot afford tier-one observability tools that may cost a million dollars.

How, he asked, should a business in Hokkaido, Jordan or New Zealand handle intent drift, blast radius, agent-to-agent communication and the transfer of governance between agents?

Hehl noted a related trap. Niche AI startups often build a narrow agentic product, only for the next frontier model or hyperscaler update to build that feature in and make the company irrelevant. For the mid-market, he argued, the answer is partnership: managed providers that deliver governance and observability as part of the service, “a Sherpa through the valley.”

Having ISO 42001 vs. living it

Bavisi pointed out that ISO 42001 has more than 190 controls, and as with ISO 27001, holding the certificate and operating by it are two different things. Do organizations show compliance for regulators while running with serious gaps?

“All the time,” Hehl said. Agentic adoption remains limited in US classified environments (IL-4 to IL-6) and in multinational bodies such as the European Space Agency, a Foresite client. In the commercial world, the most governed vertical with the largest gap is healthcare and life sciences, which holds sensitive patient data and research.

His prescription is to move telemetry into the cloud. Once workloads move from physical data centers into a hyperscaler, “the policies are native by default. And when agents are deployed, they’re governed by default.” The next big push, he said, is identity and privileged access management for agents, treating them as users. Bavisi added temporal context as another layer to solve.

Consolidation, and a $250,000 query

Bavisi argued the industry has a duty to bring along the businesses “where the actual civilization exists.” How? Hehl’s one-word answer: consolidation.

By around 2035, he expects hyperscalers such as Google, Microsoft and AWS, along with CrowdStrike, Palo Alto Networks, Cisco and to some extent Oracle, to keep absorbing companies and technologies. That will make enterprise-grade tooling usable by businesses of every size from one platform. Startups will keep flourishing, and Israeli startups will stay disruptive, but Hehl predicts acquisition values will fall as vibe coding lets almost anyone build a product. He also sees Chinese and Indian AI models closing the gap with American ones, with major implications for global security over the next 20 years.

On Anthropic’s Mythos, Hehl was blunt: it is “the greatest sales tool ever created.” It has stirred fears of boogeyman vulnerabilities, but he said it is genuinely capable of discovering zero-days and attack paths that need fixing. That in turn creates business for defensive tools such as Google’s CodeMender. None of it changes the basic discipline: find attack paths, shore them up, test, repeat. “We’re never really more than 99% of the way there.”

The other barrier for the middle market is cost predictability. Token-based pricing is not yet predictable. Hehl admitted Foresite once ran an accidental $250,000 query on Gemini during iterative testing and had to ask Google for forgiveness. Predictable pricing, he said, is what will let more customers lean in.

What really blocks the move to AI-era security

Asked about the biggest obstacle to moving an organization from traditional security to an AI-era culture, Hehl named three:

  • Financial opacity. Customers lack transparency on what AI will actually cost.
  • Unclear goals. Organizations often lack a clear picture of what they want AI to achieve, which calls for education and enablement.
  • Reluctance to hand over control. Teams that have built careers around a security posture over the past decade ask why they should introduce new pain when nothing hurts now.

Winning them over is part of the “trust motion,” he said, and some will never make the shift because of requirements that keep them where they are.

Machine-speed attacks and a post-quantum dam

What keeps Hehl up at night is machine-speed attacks, which he said “are absolutely accelerating,” and frontier models built explicitly to break software and create new attacks. Beyond that is the prospect of cryptography being cracked faster than it can be rotated.

Bavisi shared the concern. He sees cracks already forming in encryption and fears the day “the dam breaks, and we’re not ready for a PQC world.” Hehl pointed to the downstream effects on blockchain, financial systems and energy grids, and the limits of what any single defender can do.

“There’s nothing we could do as an MSSP or a security practitioner to reset global cryptography … to reset the keys that we hold dear in order for us to maintain those chains of custody, privacy, and command over those systems.”

The takeaway from the conversation, Bavisi said in closing, is that the agentic SOC “is not just in the labs. It’s already in production.” The open question is whether governance, cost models and the cryptographic foundations underneath can keep up.

Watch the full episode of The Cybersecurity Podcast by EC-Council, Episode 18, featuring Jeremy Hehl.

Jeremy Hehl, Chief Evangelist at Foresite Cybersecurity

Jeremy Hehl is Chief Evangelist at Foresite Cybersecurity, a Google Cloud Premier Partner and the 2026 Google Cloud Security Partner of the Year.

Cyber Insurance Is No Longer Optional: In the Age of AI-Driven Threats, Can Your Business Afford the Risk of Being Unprotected?

Cyber Insurance Is No Longer Optional
By Kareena Bavisi, Chief of Staff | EC-Council
It comes down to one number: what your worst incident would cost, and how much of it the business can carry on its own. With a CISO, that’s usually a risk decision. Without one, cyber insurance may only be a compliance purchase.

Before deciding whether to buy cyber insurance, a business needs one number: what its worst incident would cost. When I spoke with Gregory Eskins, who leads Marsh’s Global Cyber Insurance Center, that was where he said a first-time buyer should start. He doesn’t assume every business should buy it. He starts with how much loss a business could absorb on its own, then asks whether the cost of covering the rest makes sense.

How a business gets to that answer depends on who is making the call. Organizations with a CISO tend to be engaged and aware of the risk, and many are on a journey toward resilience. Organizations without one, whether because they lack the resources or because insurance is a compliance requirement for them, face the same decision with less security expertise in the room.

What moves clients to act is usually an event. Cyber risk, Eskins told me, has always been “intellectually understood, I would say, by most organizations, but it’s not been felt personally.” When a real incident hits the news, clients ask about it and whether they’re covered, and AI is making the risk more real to them. That is a reactive way to make a decision about money and risk.

Two kinds of buyers

Start with organizations that have a CISO. They tend to be the engaged ones, and insurers pull them into the conversation because underwriting is technical. They understand the realities of their organizations best, and many are on a journey toward resilience.

Organizations without one are a different story. Some lack the resources for a CISO. Some buy cyber insurance mainly because they have to. That does not track neatly with size or sophistication, and there is nothing wrong with it. For some smaller organizations, insurance is a compliance tool, and, as Eskins shared, buyers generally still get a good product even without understanding everything in it.

The catch is what happens next. Without someone whose job is to keep the exposure in front of leadership, cyber risk tends to stay abstract until a headline makes it real. AI is part of the reason that is changing. As Eskins puts it, “it’s in the headlines all the time. Fear is a strong motivator.” That fear is pulling more organizations into the conversation. For a company with a CISO, it lands on a process already in motion. For one without, it tends to produce a reaction instead of a plan.

Start with your own number

A cyber insurance decision is two questions in sequence. How much loss can the business absorb? And what would it cost to hand off the rest? Plenty of buyers jump to the second one and start comparing quotes, which lets a renewal date or a questionnaire set the limit instead of the company’s own exposure.

The first question needs a worst case and a figure, and the figure matters because the range is wide. Eskins frames it as whether the loss is “a 5 million dollar risk or is that a 50 or $500 million risk?” Good security shrinks that number but never erases it. Some residual risk stays with the business, and that remainder is what the decision is about.

From there it comes down to tolerance. If the business can carry the remainder comfortably, declining coverage is a defensible call. If it can’t, the policy is worth pricing, and the comparison that counts is what covering the remainder costs against what leaving it uncovered could cost. That is why insurance is optional and the number is not. In Eskins’ words, “the question is not should you buy insurance for that, but what is your risk tolerance.”

Pricing the rest is the easier half. The next step is to find out whether coverage exists for that remainder and what it would cost “relative to the risk that you would be otherwise accepting.” In many cases, Eskins says, transferring that risk is “incredibly capital efficient.” Whether it is for your business comes down to quotes and policy language, so compare quotes and read the exclusions before deciding.

AI raises the cost of weak basics

AI has not created a whole new category of cyber risk. It has made weak fundamentals more expensive. Eskins is direct about it: “The foundations and fundamentals of strong cybersecurity still matter. In fact, they’re more important than ever.” Attackers now have tools that work at speed and scale, and a company with weak basics is at a serious disadvantage against them.

The worry is running ahead of the data. So far Marsh has not seen a dramatic increase in events overall, though attack speed and scale are expected to rise, and the firm is working with clients to help them get ahead of it.

What a security team should be ready to explain follows from that. A company deploying AI agents to automate workflows needs a clear account of identity and access, meaning what each agent can reach and why, with permissioning and segmentation behind it. That is incredibly important. Keeping that account current is the working core of AI governance: which agents are running, what each can touch, and who answers for it. Faster patching helps, but it can’t be the whole answer.

AI also cuts the other way. It amplifies familiar risks, and it mitigates them too. The question for a security team is whether it is using AI itself, for reconnaissance and threat hunting, to move at greater speed and scale.

Strong security earns something steadier than a discount. Market conditions, mainly supply and demand, move price more than any single company’s controls can, but good cybersecurity and governance help a business “smooth out some of those ebbs and flows of volatility.”

What CISOs can do differently

The most useful step is also the cheapest: ask. “Do not be afraid to ask your underwriter questions,” Eskins says, about the policy itself and about the market. People on the other side are generally generous with the information they want to provide.

That openness reflects how the relationship has changed. Insurance used to be seen as a drain on the security budget, or, wrongly, as an indictment of the security program. Now, Eskins says, “it’s far more of a game of cooperation and collaboration.”

It also runs both ways. Marsh wants to hear what frustrates CISOs about claims or how a policy needs to evolve, and works on ways of dealing with it. Raising it, instead of absorbing it until the next renewal, is the other half of engagement.

The steps themselves are short: quantify the worst case, decide how much of it the business can carry, and ask the people who price the risk what it would take to cover the rest. None of it needs new spend or new tooling. In an organization without a CISO, the same steps fall to whoever owns the insurance decision.

Asking well takes fluency, and fluency is not evenly spread. Eskins told me CISOs have been great partners, asking lots of questions and wanting to learn. He also acknowledged that clients can find it harder to get educated on this, with products still evolving and a lot to digest, and he called helping them part of Marsh’s job. In my view, that points to a gap. Where a CISO is in the seat, that learning is usually underway. Where one is not, the person signing the policy may never have been taught how underwriters think or what moves a price. At bottom it is a communication gap: security teams and underwriters often describe the same risk in different terms. Training built around cyber insurance is starting to appear to bridge it, aimed at exactly that reader.

Decide before the headline does

Cyber risk will keep making the news, and each incident will keep sending clients to their brokers to ask whether they are covered. Organizations that wait for that moment decide under pressure. Organizations that have already priced their worst day, and asked an underwriter what covering the rest would cost, decide on their own terms.

Cyber insurance may be optional. Knowing what you can afford to lose is not.

Kareena Bavisi, Chief of Staff | EC-Council

Kareena Bavisi is a contributor to CISO MAG covering cybersecurity and AI, writing on how emerging technology is reshaping risk, governance, and the way enterprises defend themselves. Her work draws on conversations with security leaders, insurers, and technologists across the industry to surface the trends shaping the field. She is also Chief of Staff to the CEO and Founder at EC-Council, a global leader in cybersecurity certification and education.

Why AI Governance Is Becoming Cyber Insurance’s Next MFA

By Kareena Bavisi, Chief of Staff to CEO at EC-Council
I set out to understand why companies still don’t think they’re adequately covered. What I found looks like a pattern the insurance industry has lived through once already: multi-factor authentication went from a nice-to-have to a near-universal underwriting expectation in a matter of years, and insurers that got burned by its absence started pricing around it, denying claims over it, walking away from risks that didn’t have it. AI governance is tracing the same path, earlier, but faster. It isn’t a formal requirement anywhere in the cyber insurance market yet. But the data in this piece says it’s headed there, and the companies that treat it as optional for too long are going to find that out the same way the MFA holdouts did.

Here’s a number that should worry any CISO building an AI governance program right now: 74% of professionals who evaluate, deploy, secure, or govern AI at their organizations believe they could pass an AI compliance audit today. Only 27% describe their governance program as fully mature. That’s according to Schellman’s State of AI Governance Report 2026, a survey of 525 U.S.-based practitioners, and it’s close to an exact echo of a number I’d already been sitting with: in Munich Re’s 2026 Global Cyber Risk and Insurance Survey, 89% of C-level respondents say they don’t consider their company adequately protected against cyberattacks, up from 81% in 2021, 83% in 2022, and 87% in 2024. Two different surveys, two different questions, the same shape: confidence and reality keep drifting apart, and whatever’s being done about it isn’t closing the gap.

Bob Parisi, Head of Cyber Solutions for North America at Munich Re, has spent nearly three decades underwriting cyber risk. I asked him why. His answer wasn’t about a missing product. It was about two groups of people who don’t speak the same language.

A literacy problem, not a product problem

“Dealing with financial risk transfer is not something a CISO deals with every day,” he told me. The reverse is just as true: underwriters are pricing a technical risk they don’t observe directly, working from what a company represents about itself once a year, usually through a broker-scheduled call. “ We typically engage with them once a year when they fill out the application,” he said. Where that changes, he told me, is in companies where the CISO, CFO, and chief privacy officer are already talking to each other internally, those are the ones that end up buying coverage closer to their real exposure.

His own company’s data backs that up. Asked what’s holding back better cyber defense, Munich Re’s survey found the top two answers were both about people, not tools: “low security awareness among employees” (40%) and a lack of skilled personnel (31%), both ahead of budget. The disconnect Parisi described anecdotally is the top-ranked problem in his own company’s data.

That disconnect is what got me thinking about how to close it, and it’s why companies like the Cyber Insurance Academy exist. The talent gap behind it is well documented: insurers report more than 400,000 open positions across lines of coverage, cyber underwriting pay runs well above a typical broker’s, and the shortage persists anyway, because, as Gallagher’s Joey Sylvester has put it, “there are a very limited number of individuals specializing in this class of business” who can keep up with both the insurance market and the cybersecurity industry at once. That’s the same disconnect Munich Re’s own survey flagged as the top two defense challenges: low security awareness and a shortage of skilled people, not budget. Closing it takes skilled people who’ve actually sat through the training, courses like the CCIS, built to give security and insurance professionals enough of each other’s language that the once-a-year application conversation reflects real exposure, instead of two sides talking past each other.

What MFA can tell us about where AI governance is headed

This is where I think the Schellman number matters more than it looks on its own. AI governance right now sits roughly where multi-factor authentication sat two or three years ago, something insurers were starting to care about, but not yet something they’d decline a policy over. That’s changed fast. Brokers who place cyber coverage say MFA has gone from a minor underwriting factor to close to a hard requirement in the span of about two years. Insurers that once quoted risks without MFA, sometimes with a small premium loading, have largely stopped offering that option. Integrity’s Colin Fox says only a handful of insurers will still write a policy without it, describing it as “quite a rarity these days” in comments to Insurance Business UK.

AI, in Parisi’s words, is “first and foremost” in what Munich Re is watching right now. “It takes up a lot of the oxygen in the room in any discussion around cyber risk,” he told me. That’s not a formal policy change, but pricing is where MFA’s history is instructive. The pattern with MFA wasn’t that insurers added a new exclusion, it was that a control quietly moved from a nice-to-have to a baseline expectation, and companies that couldn’t demonstrate it found their options narrowing. Endpoint detection and response looks to be following the same track; brokers describe it as already close to mandatory for companies seeking higher coverage limits.

AI governance looks like it’s on the same curve, just earlier. 71% of C-level respondents in Munich Re’s survey now call AI significantly relevant to their business, up from 62% two years ago, overtaking cloud computing as the most-flagged technology. That’s the kind of inflection point MFA passed through before it hardened into a requirement.

What underwriters are actually scoring

No single AI tool moves the needle on its own. Deploying a specific product doesn’t earn a company a flat discount on premium. What underwriters are actually pricing, Parisi explained, is maturity, whether a company can show it’s using AI deliberately rather than just enthusiastically, and he pointed to three signals in particular.

  • Overexuberance. A security team that has rushed to deploy AI everywhere without building the process to manage it first.
  • Denial. A company that insists it isn’t using AI at all. Parisi’s read is that it’s there whether leadership acknowledges it or not.
  • Disciplined adoption. A company that treats AI as genuinely new, testing it deliberately and rolling it into production only once it’s confident the rollout strengthens the organization rather than exposing it.

“Knowledge is power,” he told me. “Anything that helps a company better understand its risk, and articulate how it’s managing and mitigating that risk, will be viewed very positively by our underwriters.” That kind of structured, documented governance work, something a company can actually point to, is exactly what reads as a green flag to him. AI governance, in particular, is shaping up as its own discipline, distinct from general security literacy, and the organizations building that competency deliberately, rather than picking it up as they go, are the ones standing out.

That same logic, structured proof of risk maturity that underwriters can actually price, is my own case for why EC-Council’s CRAGE certification and its underlying Adopt, Defend and Govern (ADG) framework exist. ADG, developed with input from 23 advisory members across the industry, and CRAGE under it train practitioners to produce exactly the kind of documented risk articulation that reads as a green flag to an underwriter, translating AI governance maturity into terms a cyber insurer can price.

Schellman’s own numbers back this up from a different angle: companies with mature AI governance programs have AI agents running in production at more than three times the rate of companies with developing programs, 78% versus 22%. The gap isn’t just about compliance paperwork. Mature governance correlates with companies that are actually further along in deploying the technology with confidence.

The bottom line

Parisi’s advice from the underwriting side was simple: stop treating the insurer conversation as a compliance form, and start treating it like a board conversation. “Think of the cyber insurers as if you were talking to your board of directors, because that’s really the perspective we’re looking at this from,” he told me. “We don’t want to know which edition of the software you’re using. We want to know you have the right processes and protocols for making sure that software is updated and deployed, and an understanding of what the potential financial loss is if it goes wrong.”

Put the two patterns side by side, and AI governance is not going to stay optional. MFA went from a footnote to a wall in about two years once insurers decided it was a real predictor of loss, and the Schellman numbers suggest the market is already sorting companies into those who can prove maturity and those who can’t. The 89% of executives who don’t feel protected today have a narrowing window to close that gap before it closes on them, and closing it looks a lot like what this piece has been circling the whole way through: literacy on one side of the table, documented maturity on the other.

Kareena Bavisi, Chief of Staff | EC-Council

Kareena Bavisi is a contributor to CISO MAG covering cybersecurity and AI, writing on how emerging technology is reshaping risk, governance, and the way enterprises defend themselves. Her work draws on conversations with security leaders, insurers, and technologists across the industry to surface the trends shaping the field. She is also Chief of Staff to the CEO and Founder at EC-Council, a global leader in cybersecurity certification and education.

What the Nuclear Treaty Got Right, and Why AI Still Can’t Use It

By Jay Bavisi, Founder & Group President, EC-Council

Nuclear treaties stopped bombs. They don’t stop a file you can email.

That’s the problem with reaching for the Nuclear Non-proliferation Treaty every time someone brings up AI risk, and last week gave everyone a reason to reach for it.

Dario Amodei published an essay arguing that the industry needs to pace itself because alignment work can’t keep up with how quickly capability is advancing now that models are helping build the next generation of models. Sam Altman agreed within hours. Elon Musk agreed in three words. Demis Hassabis agreed too and tied it back to a standards proposal DeepMind floated in July. Three men who spend most of the year competing for the same customers found, for one weekend, something to agree on.

The market didn’t wait for the philosophy to settle. By Monday, cybersecurity names including CrowdStrike, Palo Alto, Okta, Zscaler, and SentinelOne were up double digits. Semiconductor and infrastructure names sold off. Read that as the market’s actual translation of the essay: agents that can act on their own are a threat, and threats get budget. That reaction told you more about where this is headed than the essay did.

President Trump answered the next morning. The United States is leading China in AI, he said, and closed with a line that’s going to outlive most of the commentary around it: whoever wins AI wins. China’s Foreign Ministry answered within the same news cycle, calling the whole slowdown conversation fearmongering. Two governments, the only two whose cooperation a real treaty would need, both declined the premise within three days.

I’ve spent two decades building certification and training programs for the cybersecurity industry, and I’ve watched more than one generation of “we need a global framework for this” conversations play out. They rarely arrive in time to matter. So before anyone spends another year drafting a treaty that depends on Washington and Beijing agreeing to slow down together, it’s worth being honest about why the model doesn’t fit.

The NPT works, to the extent that it works, because it governs a physical thing. Uranium has to be enriched through a small number of known industrial processes, and once it is, it can be weighed, sealed, and tracked. Inspectors can put a camera on a centrifuge hall. Nobody’s smuggling a nuclear core through customs undetected.

AI doesn’t have that constraint. Once a model finishes training, what you’re actually trying to govern, its weights, is a file. It moves at the speed of a network connection. It can be copied to a laptop and walked across a border in someone’s bag. That isn’t hypothetical. It’s close to what Anthropic’s own threat reporting describes happening today. There’s no equivalent of a Significant Quantity for a neural network. No inspector can walk into a data center and certify that a company hasn’t quietly fine tuned an unmonitored version of its flagship model on a cluster the treaty never knew existed.

Even judged solely on its own terms, the NPT never reached full compliance. Four nuclear armed states sit outside it. If the best verification regime in history, pointed at the easiest possible target, still has holdouts, a treaty aimed at software is not something I’d bet a security program on.

So set the treaty down. Look at where the risk is actually piling up, because it isn’t sitting in a policy draft somewhere in Geneva. It’s sitting in poured concrete.

Global data center spending is on track to clear a trillion dollars this year, making this the largest infrastructure buildout in corporate history, and it isn’t pausing for anyone’s governance conversation. Microsoft, Amazon, and Google are signing nuclear power contracts spanning multiple decades just to keep the buildout fed. Every one of those facilities will eventually run agentic systems with real authority inside finance, healthcare, logistics, and government.

That’s the proliferation problem that actually matters. It is not weapons grade material crossing a border. It is compute, capital, and decision making authority scaling faster than most organizations’ ability to watch what they’ve built.

I’ve sat in enough boardrooms this year to put a name on the pattern: shoot first, ask questions later. A division stands up an agent to handle claims, customer support, or code review, and the governance conversation happens after the incident report, not before the rollout. IBM’s most recent Cost of a Data Breach Report puts a real number on this. Unsanctioned AI use, often called shadow AI, adds roughly $670,000 to the average cost of a breach and shows up in about one in five breaches studied. Netskope’s own 2026 data finds that a large share of enterprise generative AI traffic still runs through personal, unmanaged accounts, bypassing every control a security team thinks it has in place.

Companies are adopting this technology at press release speed and governing it at compliance calendar speed, and the gap between those two speeds is where the losses live.

A tool is not a control. I’d put that on a slide in front of any board that thinks buying an AI platform closed this risk out. It’s the 2026 version of buying a firewall in 1999 and calling the network secure. The firewall did something. It didn’t do the thing you needed.

Three words have started organizing how I think about the sequence a responsible rollout actually follows: Adopt. Defend. Govern.

Adopt, because sitting the technology out isn’t caution. It’s a slower way to lose ground. Defend, because the moment it’s live, you’re running a system capable of independent action inside your most sensitive environments, and it earns the same seriousness you’d give any system that touches money or patient data. Govern, because defense without governance is a fire extinguisher nobody’s assigned to check.

Say those three words enough times in a planning meeting, and they start reordering the meeting itself. Adoption comes first, but never by itself.

Underneath that sequence is a harder shift than most executives have made yet: moving from certifying a system once to governing it continuously. Traditional compliance assumes deterministic software. You test it, confirm that it behaves the same way every time, certify it, and revisit it next year. Agentic systems break that assumption at the root.

Run the same agent against the same task twice, and you can get two different sequences of action because the behavior is nondeterministic by design. A certificate from launch day tells you almost nothing about what that system does 200 days later, after it’s been fine tuned, wired into new tools, or handed a task nobody scoped for it up front.

That’s what people mean when they talk about a harness and context, and it’s worth saying plainly instead of leaving it as jargon. A harness is the boundary an agent operates inside. It defines what data the agent can touch, what it can do without a human, and where an action has to stop and wait for sign off before something irreversible happens.

Context is the evidence sitting underneath every one of those actions. It captures not just what the agent did, but why it was allowed to do it, under whose authority, and against which control. Put the two together, and you get something neither a treaty nor a one time audit can give you: a system that’s still governable while it’s running, not just at the moment somebody signed off on it.

None of this works without people who know how to run it, and this is where most enterprises are underinvesting relative to what they’re spending on the technology itself. I’d point to three groups.

Security teams need offensive AI ability as a baseline skill, not a specialty. For 20 years, training taught the profession to find the vulnerability, patch the system, and watch the logs while defending a target that behaved predictably. That skill isn’t going away, but it stops being sufficient once the thing you’re defending, and increasingly the thing attacking you, can act on its own and adapt during an attack.

The practical version of this is red teaming AI systems the way a penetration tester tests a network. That means testing whether a model can be manipulated through prompt injection and whether an agent’s runtime defenses hold once it’s actually wired into production tools. Anthropic’s own disclosures this year describe autonomous agents attacking targets nobody instructed them to attack. Most job descriptions haven’t caught up to that yet.
Program and IT managers need to run the full AI lifecycle, not just the launch. Most technology leaders were trained to manage a system from procurement through launch and consider the job largely finished at that point. AI doesn’t hold still that way.

Design and requirements, build and integration, deployment and change management, and incident response when something breaks all have to run as one continuous cycle because the system keeps changing after it ships. A manager who can run that whole loop is worth more right now than one who can only run the handoff.

Risk and compliance professionals need real fluency in AI governance and, specifically, in responsible AI and ethics treated as a discipline with its own evidence standard rather than as a checkbox bolted onto an existing audit. That means learning to score AI specific risk, including technical, fairness, operational, and emergent risk, against a maturity scale rather than treating it as a yes or no question.

It also means learning one methodology that maps across the frameworks companies actually get judged against: NIST’s AI risk framework, ISO’s AI management standard, the EU AI Act, and sector regulators, each using its own vocabulary for what is often the same underlying control. A risk officer who can walk into any of those conversations without starting from scratch is the difference between governance that survives an audit and governance that only survives a slide deck.

If you can’t tell me, right now, what your AI systems did in the last hour and why they were allowed to do it, you don’t have an AI program. You have a liability with good marketing.

And if the three people who’d need to answer that question, your security lead, your program manager, and your risk officer, haven’t been trained to actually answer it, the gap isn’t the technology. It’s talent, and talent is the faster problem to fix.

The treaty conversation will keep running in the background, and it should. People who work in arms control full time are better positioned than I am to carry that debate forward. But it’s not the conversation that decides whether your company has a governance failure next quarter.

That one comes down to whether the agents already running inside your business operate within a harness someone can see and are monitored by people trained to watch them, or whether they operate outside one, quietly, until the incident report explains what happened after the fact instead of before it.
There’s a harder version of this question waiting beyond the one I’ve answered here. If the capability gains Amodei described keep compounding the way he says they are, governance itself may need to be rethought from the ground up. The question will no longer be just who’s watching the agent, but what oversight even means once a system can improve itself faster than any team can review it.

That’s a different piece, and it’s coming next.

EC-Council Releases ADG 2.0 and Offers Its AI Governance Crosswalks Free to Every Government, Regulator, and Standards Body as Nations Split on AI Rules

With autonomous AI agents already reaching into government systems and no global treaty in sight, EC-Council will assist any public authority that asks to map ADG 2.0’s 180+ controls to its own laws and standards

TAMPA, Fla., October 1, 2026: EC-Council today released ADG 2.0, the second version of its Adopt, Defend, Govern framework, with crosswalks to more than 90 regulations, standards, and global frameworks at no cost to any government, regulator, or standards body in the world. EC-Council will also assist any government, regulator, or standards body that reaches out, helping officials apply ADG 2.0 to their own laws, draft rules, and national standards, consistent with applicable law.

The offer comes as governments split on how to govern AI. President Trump has said concerns about AI risks are being exaggerated. China’s Foreign Ministry said that fearmongering, confrontation, and vicious competition would hamper sound global AI governance, and on September 26, Singapore’s Foreign Affairs Minister Vivian Balakrishnan urged the UN General Assembly to establish new multilateral rules, suggesting a UN Framework Convention on AI Safeguards.

Meanwhile, autonomous agents are already crossing boundaries. In July, OpenAI disclosed that models undergoing an internal cybersecurity evaluation had circumvented their testing environment and compromised parts of Hugging Face’s production infrastructure. Last week, Australia’s Prime Minister said an OpenAI agent had gained unauthorized access to a public-facing Medicare statistics portal in June. OpenAI also confirmed its agents had accessed U.S. government websites in ways it neither planned nor approved and paused training of its latest models. Separately, an independent research lab also reported a failed attempt against a Department of Education website.

“The nuclear non-proliferation treaty worked because inspectors could weigh uranium,” said Jay Bavisi, Founder, Chairman and Group CEO, EC-Council. “You can’t weigh an agent’s behavior, and no AI treaty will be signed and ratified in time to protect the systems agents are reaching today. Governments shouldn’t have to build governance from a blank page while they wait. ADG 2.0 is open, it’s already mapped to the rules they’re working with, and we’ll sit down with any government, regulator, or standards body that wants help putting it to work.”

ADG 2.0 replaces the original 12 minimum controls with more than 180 controls in 12 control families, mapped to more than 90 regulations, standards, and frameworks, including NIST AI RMF, ISO/IEC 42001, and the EU AI Act. For a regulator, the crosswalks show how a proposed national rule lines up against what other jurisdictions already require. For an enterprise, they mean working on one control set and showing how that work satisfies every rulebook it answers to, even as those rulebooks diverge.

“Most AI governance today is a policy document nobody tests,” Bavisi said. “ADG 2.0 asks for something harder. Say what you do, do what you say, and be able to prove both on any given day. That’s what a regulator, an insurer, or a board will ask for after the next incident. There will be a next incident.”

ADG is stewarded by EC-Council’s 35-member AI Advisory Board, comprising practitioners who build, secure, transform, and govern AI across some of the world’s leading organizations such as Salesforce, ServiceNow, Azure, Microsoft, Citi, JPMorgan Chase, NTT DATA, Jio Platforms, BNP Paribas Wealth Management, and others.

The framework includes:

  • 180+ controls in 12 control families. One catalog covering what needs to be governed across the AI life cycle, from design and data through runtime monitoring, incident response and third-party risk.
  • Mappings to 90+ regulations, standards, and frameworks. Crosswalks to sources including NIST AI RMF, ISO/IEC 42001, the EU AI Act, MITRE ATT&CK, MITRE ATLAS, the OWASP Top 10 for LLM Applications, and the OWASP Agentic Top 10.
  • Runtime governance. Controls at four points in how an AI system operates, ADMIT, DECIDE, EMIT, and COMMIT, with human approval required at defined decision thresholds.
  • Tiered autonomy. Assistive, Conditional, and Autonomous levels set out what an AI system may do at each level, when a human must review, and what evidence must be kept.
  • Defined evidence requirements. Each control specifies the evidence it must produce, so organizations collect it while systems run rather than assemble it before an audit.
  • DART implementation method. Discover, Analyze, Report, and Transform takes an organization from finding governance gaps to closing them.

Governments, regulators, and standards bodies can request assistance at [email protected]

The ADG 2.0 framework, crosswalks, and AI Readiness Self-Assessment are available free to everyone, without registration, at AI Governance Framework (ADG) | Enterprise Agentic AI Controls

About EC-Council

Founded in 2001, EC-Council is a global cybersecurity and AI organization. It created the Certified Ethical Hacker (CEH) program and has certified more than 400,000 professionals across 174 countries. Its credentials are used across government, defense, enterprise, and academia.

As an ISO/IEC 17024 accredited certification body, EC-Council’s credentials are recognized by the U.S. Department of Defense under DoD 8140 and are held by professionals across the U.S. Army, Navy, and Air Force.

The EC-Council ecosystem includes EC-Council, EC-Council University (ECCU), CodeRed, EC-Council Global Services (EGS), EC-Council Academia, and AwareX.

Website: www.eccouncil.org

Media Contact: [email protected]

About the ADG Framework

EC-Council’s Adopt, Defend, Govern (ADG) framework is a practitioner-led operating model for AI governance. Its three pillars connect board level oversight with operational and technical controls across the AI life cycle. ADG 2.0 contains more than 180 controls in 12 control families, with crosswalks to more than 90 regulations, standards, and frameworks. It is stewarded by EC-Council’s 38 member AI Advisory Board and supported by readiness assessments and implementation guidance. Each AI Advisory board member serves in their personal capacity, and organizations are listed purely for identification. The framework, crosswalks, white paper, and self-assessment are available openly without registration or licensing requirements.

Stop Reviewing Faster: A Practical Model for AppSec at AI Speed

Close Up of a Professional Office Specialist Working on Desktop Computer in Modern Technological Monitoring Control Room with Digital Screens. Manager Typing on keyboard and Using Mouse.
By Aparna Ash Himmatramka

A developer using an AI assistant can ship a feature in an afternoon. In many organizations, the security review for that feature can still take a week.

Believe it or not, most AppSec programs are still built this way. BSIMM16, which studied 111 organizations, found security feature review in 80.2% of them. Those reviews made sense when teams shipped quarterly. Today they’re the bottleneck. What this means is that teams only review their crown jewels, and everything else goes unexamined.

Having worked in the AppSec field for more than 8 years now, I see this problem repeated over and over again. Until I realized it’s not a technical problem, it’s a gap of how we measure success for these programs.

The usual response is to make reviews faster: better triage, more AI in the review queue, one more hire. It helps for a while, but it doesn’t fix the real problem. Any program that needs a human decision for every change will grow with engineering output, and engineering output is now growing faster than any security team can hire.

So the goal shouldn’t be faster reviews. It should be needing fewer of them.

The model: humans set policy, the environment enforces it

In this model, no security engineer approves or blocks a deployment. Instead, the security team builds the systems that make those decisions automatically. We need a couple of pillars established for this model to run smoothly:

  • Secure defaults. Templates, libraries, and infrastructure modules make the safe choice the easy choice, so fewer vulnerabilities get written in the first place.
  • Automatic risk scoring. Every change gets a risk score based on what it touches and how critical the asset is. Low-risk changes move fast. High-risk changes get a deeper look.
  • AI-filtered findings. Models grounded in your own codebase remove false positives and suggest fixes that look like your developers wrote them.
  • Assurance scores instead of approvals. Every service has a health score that the deployment pipeline reads directly. Healthy services ship freely. Unhealthy services face limits proportional to their risk.
  • Feedback loops. Recurring findings become new secure defaults. When a security engineer makes a call on a new issue, that call becomes a rule, so nobody has to make it twice.

The last one is what makes everything else work. It’s surprising how many programs fix the same bug class over and over across different teams. A program that learns eliminates the class.

What this looks like in practice

Every piece of this model has already worked at scale somewhere. What’s new is putting them together, with AI connecting them.

Netflix replaced reviews with a paved road. Their AppSec team concluded that per-app security assessments didn’t scale anymore and invested in secure-by-default frameworks and self-service guidance instead. One example is Wall-E, a gateway that handles authentication for web applications. The team rewrote their checklists to push developers toward Wall-E as a pre-approved default. When a team couldn’t use it, the security team extended Wall-E instead of granting an exception. By 2021, Wall-E fronted over 350 applications, and its adoption rate was on every leader’s security risk dashboard. Notice that accountability came from a metric, not an approval.

Google shows what happens when you design a vulnerability class out instead of catching it earlier. This isn’t shift left. Shift left moves detection closer to the developer, but someone still has to find and fix every bug. Google moved responsibility for safety away from the developer and into its languages, libraries, and frameworks. Before 2012, frontends like Gmail had a few dozen XSS vulnerabilities a year. After refactoring onto safe-by-design frameworks, that dropped to near zero. This month, an internal Google AI agent found over 500 XSS vulnerabilities across Google’s broader web applications, and only two across the hundreds built on its hardened frameworks.

And this isn’t just for tech companies. Capital One, a heavily regulated bank, built its own policy-as-code guardrails in 2016 to monitor and automatically fix cloud configurations against enterprise policy.

In none of these cases did the review get faster. The need for it went away.

Three lessons for security leaders

1. Accountability doesn’t require approval. The first question every executive asks is: if nobody signs off, who is accountable? The answer is consequences tied to risk. A service that stays unhealthy loses privileges that would increase its exposure, like frequent deploys, new dependencies, or autoscaling. At planning time, security health sits right next to reliability, and teams below the threshold get capacity reserved for recovery. When the constraint comes through engineering planning, it’s no longer a negotiation with the security team.

2. Measure what the program no longer has to do. Counting completed reviews rewards the wrong thing. The single most useful metric is the human touch rate: the percentage of changes that need any security engineer involvement. It should go down every quarter. Pair it with time to
remediate critical findings and the false positive rate. A falling touch rate is also how security coverage grows without headcount growing at the same pace.

3. Plan for the AI being wrong. The worst case is a false positive that blocks a team who knows the finding isn’t real. Therefore, give developers a fast way to escalate. The disputed finding should stop counting against them immediately, a security engineer should review it within hours, and every decision should go back into the model. Also, be honest about what automation can’t do. It doesn’t reliably catch business logic flaws, design complex authorization, or decide how much risk the business should accept. That’s where your senior security talent belongs, and this model gives them the time for it.

Where to start

Don’t start with AI. Models need your organization’s data to be accurate, and that data comes from the basics: consistent scanning, an inventory of services with criticality and data classification, and simple rule-based risk scoring. Make assurance scores visible before you enforce them. Add machine learning once you have several months of history. Expect about a year before the program starts improving on its own.

The takeaway

AppSec used to be measured by how many reviews it did. At AI speed, the better measure is how few reviews the organization needs while risk goes down and delivery speeds up. Make the secure path the easiest path, and let the environment do the enforcing.

About the Author

Aparna Ash Himmatramka leads an application security engineering team at Amazon.com. She will present this model at Hacker Halted 2026. The views expressed are her own.

Why I Go to the Dark Web Every Day

By Alex Holden, Chief Information Security Officer Hold Security

For nearly two decades I made the Dark Web a place to be; however, I am a hacker nor a cyber threat actor. I am a cyber threat intelligence professional with a deep passion for cybersecurity.

After spending a decade in the 2000s in corporate security, I realized that as a CISO, I neglected to understand the enemy. The bad guys who are actually trying to hack our systems.

We spent significant amounts of time and resources building defenses. We worried about vulnerabilities, malware, compliance, firewalls, authentication, policies, and the latest technologies promising to make us more secure. But too many of our efforts were driven by reactions to ongoing attacks, fears, industry headlines, and sometimes fear-mongering from vendors.

We were looking at cybersecurity almost entirely from our side of the battlefield.

Turning my eyes toward the Dark Web helped me understand the enemy and scale defenses to the actual threats and approaches being used against us.

Just think about how quickly technology evolves. Tools change. Encryption changes. Evasion becomes more sophisticated. Infrastructure becomes distributed and disposable. Today, AI is accelerating portions of that evolution even further. Our ability to defend gets tougher because the technology on both sides continues to change.

What stayed relatively static?

The threat actors themselves.

They are people too and they dwell amongst us in our society. They make mistakes. They have goals and motivations. They have egos. They need money or information. They trust the wrong people. They get angry. They compete with each other. They brag about success and complain about failure.

And criminals need other criminals.

A successful cyberattack is rarely just somebody sitting alone in a dark room wearing a hoodie. There is an ecosystem behind cybercrime. Someone develops malware. Someone finds vulnerabilities. Someone obtains access. Someone sells access. Someone steals information. Someone launders money. Someone negotiates ransomware payments. Someone buys the stolen data.

Those relationships create intelligence opportunities.

Going to the Dark Web gave me an opportunity to learn about the enemy and gave me more tools to deter and prevent cyberattacks and breaches.

It also taught me that you cannot understand cybercrime simply by collecting indicators of compromise.

An IP address can tell you where something happened. A malware sample can tell you how something works. Logs can tell you what happened inside your environment.

But none of those necessarily tell you why you were attacked, what the criminal was looking for, what they succeeded in taking, what they plan to do next, or whether they are coming back.

For that, sometimes you need to understand the people behind the attack.

Running a diverse team that engages threat actors and keeps an eye on the pulse of the Dark Web allowed us to discover and investigate many of the highest-profile breaches of all time.

Sometimes it means getting inside a ransomware gang and understanding how they function. Who actually makes decisions? How do they choose victims? How much information do the people negotiating with the victim really have? If a victim pays, should they rely on the word of criminals that the stolen information will really be deleted?

Sometimes it means understanding the threat actor’s view of a breach. Companies naturally investigate incidents by looking inward. What systems were compromised? What logs exist? What data could have been accessed?

But attackers may already be talking about what they actually obtained.

That creates a completely different perspective.

There is an enormous difference between information that could have been stolen and information that actually was stolen. There is also a difference between what criminals claim to have and what they really possess. Understanding that difference requires more than automated monitoring. It requires context, history, access, and understanding how these communities operate.

The same applies to tracking nation-state threat actors. Their motivations are different, but they are still people operating within organizations and relationships. Watching their dynamics, infrastructure, interests, tools, and targets helps us understand not only what happened yesterday, but what may become important tomorrow.

There is another misconception about the Dark Web that is especially important.

There is a lot of stolen data on the Dark Web, but data stolen does not always translate to data abused.

That distinction creates opportunity.

Credentials may be stolen before somebody uses them. Corporate access may be offered for sale before ransomware operators purchase it. A database may circulate privately before criminals figure out how to monetize it. Sensitive information may appear in one criminal community before spreading into many others.

Once data has been weaponized, our options become much more limited.

Before that happens, there may still be time.

We can reset credentials. Disable accounts. Investigate compromised systems. Change access rights. Notify organizations. Watch for fraud. Understand how the compromise happened and prevent the next stage of the attack.

This is one of the reasons simply searching the Dark Web for a company name is not enough.

The real question is not just, “Is our data there?”

The questions are: Who has it? Where did it come from? How recent is it? Is it legitimate? Who else is interested in it? What can they do with it? And perhaps most importantly, what are they likely to do next?

That is where information starts becoming actionable intelligence.

The Dark Web has changed considerably during the years I have been watching it. Communities disappear. Criminal marketplaces get seized. New communication platforms replace old ones. Criminals move between public forums, private channels, messaging applications, invitation-only communities, and direct relationships. Artificial Intelligence…

The location changes.

The need for criminals to communicate, collaborate, sell, buy, recruit, and monetize does not.

That is why understanding the Dark Web is not about knowing the address of a few criminal forums. It is about understanding the ecosystem.
The bottom line that every cybersecurity professional needs to be aware of is that the Dark Web is a pulse of cybercrime.

Ignoring it today is dangerous.

You do not need to become a Dark Web investigator yourself, but somebody in your security strategy needs to understand what is happening outside your network, not just what your security tools are telling you from inside it.

I learned a long time ago that defending only from your own point of view leaves a significant blind spot.

Therefore, I go to the Dark Web every single day.

To understand the enemy. To see what is changing. To find opportunities before vulnerable or stolen information becomes weaponized. To defend. To deter.

And to make the cyber world a bit better.

The Cyber Security EXPO is the only dedicated recruitment event for Cyber Security Professionals

Located in the heart of London at the QEII Centre, the Cyber Security EXPO London offers a prime opportunity for cyber security professionals to connect and explore career opportunities. Exhibitors gain direct access to a qualified, targeted audience, enhancing visibility and creating valuable networking possibilities, while visitors can meet potential employers and discover new career paths that match their skills.

Atlanta Set to Host Hacker Halted and Global CISO Forum 2026, Uniting Practitioners and C-Suite Leaders

The Westin Peachtree Plaza will anchor a week of hands-on training, offensive security research, and closed-door executive dialogue as EC-Council’s flagship events return to Georgia this October.

Atlanta will once again become a gathering point for the global cybersecurity community this fall, as EC-Council brings Hacker Halted and the Global CISO Forum back to the city for 2026. Running alongside each other at The Westin Peachtree Plaza in downtown Atlanta, the two events pair a practitioner-focused technical conference with an invitation-only executive summit, giving the industry both the hands-on depth and the strategic dialogue it needs heading into 2027.

Hacker Halted’s pre-conference training runs October 4 through 7, with the main two-day conference following on October 8 and 9. This year’s theme, “Cyber Carnival: Where Illusion Meets Intrusion,” leans into the growing role deception plays on both sides of the security fence — from adversaries weaponizing phishing kits and deepfakes to defenders learning to spot the sleight of hand before it costs them. It’s a theme that shows up directly in the speaker lineup: Perry Carpenter, KnowBe4’s Chief Deception Strategist, is among more than two dozen confirmed speakers, alongside Gary Hayslip (CISO in Residence, Halcyon.ai), Alex Holden (CISO and Founder, Hold Security), and practitioners from Amazon, Microsoft, Citi, KPMG, and Coupa Software. The agenda promises more than 20 sessions spanning offensive security, threat intelligence, AI and LLM security, incident response, and enterprise cyber risk, along with the hacking villages and breakout tracks Hacker Halted has built its reputation on.

Running on the same October 8–9 dates, the Global CISO Forum enters its fourteenth year as EC-Council’s closed-door venue for senior security leadership. Attendance is deliberately narrow: CCISO certification holders and students, EC-Council-invited practitioners, and director-level or above information security executives, with marketing and business-development attendees excluded by design. That restriction is the point. The Forum is built around closed-door sessions, curated roundtables, and off-the-record peer exchange, creating space for CISOs to talk candidly about failures and lessons learned in a way that’s harder to do on a conference main stage. For executives who qualify, it functions as the strategic counterpart to Hacker Halted’s technical program — same week, same city, different altitude of conversation.

EC-Council is also widening the door to both events this year. The organization announced complimentary conference admission to Hacker Halted and the Global CISO Forum for U.S. military and government cybersecurity professionals, covering entry to the main events while pre-conference training and optional programs remain separately priced. “Cybersecurity professionals serving government and national defense operate in environments where trust, judgment and clarity are essential,” said Jay Bavisi, EC-Council’s Founder and Group President. Phil Lopez, EC-Council’s Director of DoD/Military Affairs, framed the initiative as a matter of access: “This is about removing barriers and connecting them with practical education, trusted peers and the broader cybersecurity community.”

Taken together, the two events reflect a deliberate structure EC-Council has refined over more than a decade: a public, practitioner-driven conference that surfaces the latest offensive and defensive research, running in parallel with a private executive forum where that research gets translated into strategy. For CISOs, the pairing means a single trip to Atlanta can cover both ends of the job — staying current on how attackers are actually operating, and comparing notes with peers who are wrestling with the same board-level pressures.

Registration for Hacker Halted 2026 is open now through the event’s official site, with military and government professionals encouraged to inquire about complimentary admission directly. Global CISO Forum attendance remains by invitation, extended through EC-Council and its CCISO community.

CISO MAG members get a $49 pass to Hacker Halted and the Global CISO Forum, plus 20% off training at Hacker Halted Academy before the conference.

Use code CISOMAG49 at checkout for the $49 conference pass – Register Here

Use code CISOMAG20 at checkout for 20% off Hacker Halted Academy training – Register Here

From Awareness to Relevance: Rethinking How We Teach Cybersecurity

Finance, hands and tablet screen with business people in office boardroom for investment opportunity. Growth, portfolio and presentation with financial team in workplace for wealth management.

Cybersecurity lessons have a better chance to endure when people first understand how the same risks affect their own lives.

By Jim West, based on the “inSECURITY” session for Hacker Halted / Global CISO Forum 2026

“Security awareness asks people to remember a rule. Relevance gives them a reason not to forget it.”

The Problem Is Not Awareness

Most people in an organization, from new hires to executives and security professionals, do not need another definition of phishing. They have heard the litany of warnings about suspicious links, password reuse, multifactor authentication, data handling, social engineering, and keeping software up to date. The problem is not that cybersecurity organizations have failed to distribute information; rather, the information is too often delivered from the business’s perspective rather than from the perspective of the person expected to remember it.

Each year, during compliance training, employees are told to protect corporate data, preserve customer trust, reduce ransomware risk, and follow the policies. While these goals are important for any business, they can feel distant and disconnected from the decisions an individual makes during the workday. Once the annual module ends and the quiz is passed, the lesson competes with every other demand for that person’s attention.

Completion is not comprehension, and comprehension is not retention. If the goal is lasting behavior change, awareness alone is not enough. Cybersecurity must become relevant.

Make It Personal Before You Make It Corporate

The central idea behind my inSECURITY session at Hacker Halted and the Global CISO Forum is simple. Regardless of role, title, or technical expertise, people are more likely to remember cybersecurity principles when they first understand why those principles matter to them personally.

Consider password reuse. A corporate training module may explain credential stuffing and instruct users not to reuse passwords. That is technically correct, but it starts with the organization’s concern. A relevance-based approach starts somewhere more personal for each attendee. What happens if the password from a forgotten website is exposed? What else could it unlock – their personal email, shopping accounts, social media, cloud photos, or financial information?

Once a person sees the consequence in their own real life, the organizational connection requires very little explanation, and the parallels begin to draw themselves. Replace the personal account being compromised with a corporate account, and the principle is the same. The learner has not merely memorized a rule; the learner understands the reason why the rule exists.

The same approach works with social engineering. Rather than beginning with business email compromise, we should be asking how much a stranger could learn about us as a person from a few minutes of searching publicly available information. A child’s or pet’s name, a favorite sports team, a recent trip, an employer, a colleague, or a supervisor can all become ingredients in a convincing message. When participants recognize how easily their own public information can be contextualized and weaponized, “verify before you trust” stops sounding like corporate policy and starts to sound a lot more like common sense.

Participation Turns Information Into Experience

Participation is the reason why inSECURITY is an interactive event. Rather than asking an audience to consume slides passively, attendees are asked to make personalized choices and answer questions tailored to their point of view to help reveal common assumptions we make and work through familiar situations that connect to real-world cybersecurity concepts, using the connections they’ve made to their personal lives.

For example, I can ask a room to consider where they reuse a password – or even a variation of one – and then progressively identify which personal accounts that credential could expose. Before technical concepts such as credential stuffing or the risks of predictable password variations become the focus, participants have already mapped the threat to their own money, identity, privacy, and memories. The concept is no longer abstract.

Participation creates experience; experience creates relevance. Relevance gives understanding and retention a better chance to take hold. Those better decisions can reinforce a stronger security culture.

Cybersecurity Is Already Part of Daily Life

Each inSECURITY session demonstrates how individuals are already making access-control decisions. When they decide who enters their home, they’re implementing access controls. When they share a restaurant recommendation freely or protect a banking PIN during a transaction, they are already making intuitive information-classification and handling decisions. Audience members understand resilience when they keep copies of irreplaceable family photographs in more than one location because they know a phone, laptop, or home can be lost.

The opportunity for security leaders is not merely to invent analogies, but to reveal the cybersecurity principles people already use. Instead of beginning with a framework and searching for a relatable example, begin with a relatable experience and reveal the framework within it to build stronger associations, encourage more durable recall, and reframe how people think about cybersecurity in daily life.

Shifting away from a business-first perspective when teaching cybersecurity principles changes the learner’s role, from a passive recipient of security rules to a practitioner. They recognize the principle, test it against their own experience, and apply it in a new context. The real test does not occur during the course; it occurs days or months later, when a person must make a decision without a training slide telling them what to do.

What CISOs Should Change

Making this shift does not require abandoning compliance training, phishing simulations, or established awareness programs. It requires changing the objective from “Did people complete the training?” to focusing on “What will trainees still remember when the training is no longer in front of them?” Relevance-based learning does not replace technical controls or secure-by-design systems; it strengthens the human layer that must make decisions where the technology cannot make them automatically.

  • The overarching goal of cybersecurity awareness programs is to influence human behavior and decision-making. Humans rarely make those decisions because a compliance dashboard is green. People are more likely to change behavior when the reason behind the behavior feels meaningful, relevant, and applicable to the decisions they actually make.
  • By starting with an individual-first perspective in training, every lesson should answer “Why does this matter to me?” before asking “What does the organization require?”
  • Designing participation in the lesson asks people to choose, predict, identify, and/or respond rather than just define or explain the security principle.
  • Revisit concepts 30, 60, or 90 days later instead of relying only on an immediate post-training quiz to measure retention of the information over an extended period of time.
  • Measure behavior, not just physical or virtual attendance. Looking beyond completion rates to reporting, recognition, repeat risky behavior, and other indicators shows whether decisions and user behaviors are changing and whether they have changed in response to the effectiveness of the training.

From Awareness to Relevance

Organizations will always need policies, controls, and mandatory training. But the most effective lesson may begin before we mention the organization at all.

Show people how cybersecurity protects their money, privacy, identity, family, reputation, memories, and choices. Let them experience the concept. Then connect that same lesson to the systems and information they protect at work.

Before asking people to protect what matters to the business, show them why cybersecurity matters to them. The goal is not for people to remember the cybersecurity training. The goal is for them to remember the cybersecurity lesson when the training is nowhere in sight.

About the Author

Jim West is a cybersecurity leader, educator, and speaker whose work focuses on making complex security concepts practical, memorable, and relevant for audiences at every organizational level. His interactive “inSECURITY” session is being presented at Hacker Halted / Global CISO Forum 2026.

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