CVE-2026-101767 is an unauthenticated SQL injection that can be exploited for remote code execution.
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CISA added the Brotli flaw to its KEV catalog, and two honeypot operators logged attempts, including one that used a broken exploit.
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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.
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.
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.
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.”
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.
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?”
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.”
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.
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.
Asked about the biggest obstacle to moving an organization from traditional security to an AI-era culture, Hehl named three:
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.
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.
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.
The post Who Do We Fire When the Agent Gets It Wrong? Inside the Agentic AI SOC appeared first on CISO MAG | Cyber Security Magazine.
AI is opening new pathways to information, giving people faster access to answers, ideas, and knowledge. At the same time, it’s making it harder to know whether the content we see is authentic.
Companies that build AI have a responsibility to make it reliable and trustworthy, and that responsibility doesn’t shift to the people who use it. But AI is already shaping how people search, learn, and encounter information. Helping people navigate that reality means building media and AI literacy (MAIL): the judgment to decide whether and how to use AI and what information to trust. These skills are as foundational as AI safety, and educators are asking for help teaching them.
That’s why, during Global Media and Information Literacy Week, we’re launching the expanded AI Classroom Toolkit: Unlocking Generative AI Safely and Responsibly, created with the online safety organization Cyberlite. It’s part of Microsoft’s broader commitment to make sure AI strengthens human thinking rather than replaces it. It also reflects Microsoft’s focus on education and empowerment, a core pillar of our Safe Participation Framework.
The expanded toolkit adds a deeper focus on critical thinking, verification, and media and AI literacy, helping students judge AI outputs and make informed decisions about what to trust online. Together with the original modules, it’s now a single, classroom-ready resource that covers the core of AI safety and literacy. Realistic, scenario-based lessons and activities help students practice these skills in situations they may encounter online:
“When we created the first Classroom Toolkit: Unlocking Generative AI Safely and Responsibly with Microsoft in 2023, most of the conversation was about using AI safely. Today, the questions young people ask about AI have grown sharper and more personal, like what to believe online or how far to trust a chatbot’s answer,” said Michelle Yao, cofounder of Cyberlite.
The toolkit also reflects what young people in our co-design sessions with Cyberlite told us: they want to learn by doing, through games, real-life scenarios, and peer discussion. Teachers don’t need to be AI experts, and students don’t need an AI tool in hand to start.
That approach builds on the reach and results of the original toolkit. Since 2023, the first version has reached educators in 10 countries and 11 languages, including through government and partner learning platforms. In South Korea, 93% of participating educators reported improved knowledge and understanding of ethical issues, privacy, and bias related to AI.
“Our hope is that every student leaves a little surer of their own judgment, and that everyone feels more confident deciding how AI fits into their lives,” said Yao.
Education is one part of the equation. Our commitment to media and AI literacy also shows up in how we design products and the policies we support.
Last month, we set out five principles for AI in education, including “AI designed to support students’ learning, not replace their thinking.” We also committed to enforceable privacy, safety, and transparency protections for qualifying educational AI products in US school districts. These protections help schools and families understand how these tools work and the controls available to them.
The Organisation for Economic Co-operation and Development has released a framework for a MAIL assessment beginning in 2029. As part of its Programme for International Student Assessment (PISA), it will examine how students reflect on, analyze, and evaluate information, including AI-generated information. This week in New Delhi, UNESCO is also launching an AI extension of its “Think Critically, Click Wisely” global curriculum, which Microsoft advised on.
This growing global focus makes the need for support urgent—educators want help, and many aren’t getting it. In Microsoft’s 2026 AI in Education report, 53% of educators and 77% of students say they haven’t had AI training.
Closing this skills gap matters beyond the classroom. Healthy societies depend on a shared basis of facts, informed public debate, and confident civic participation. As authenticity gets harder to judge, there’s a risk that people start doubting everything, creating what the Digital Inquiry Group calls a “dangerous declaration of permanent uncertainty.” MAIL offers a way through. It builds the confidence to decide what to trust.
Across Microsoft, we’re helping people build these skills wherever they learn and find information:
What’s next
Through Microsoft Elevate for Educators and local partners, we’ll bring the AI Classroom Toolkit to new audiences, with more languages and localized content rolling out through 2027. We’ll keep evolving it as technology and educators’ needs change.
Explore the toolkit and supporting resources for educators today.
AI will keep changing how people find information and the content they encounter. Our goal is to help people meet that change with confident, informed judgment about the AI they use and the information they trust so they can fully take part in civic and democratic life.
The post In an age of AI, deciding what to trust is a foundational skill. Here’s how we’re helping educators teach it. appeared first on Microsoft On the Issues.
The rapid diffusion of AI globally and the pace at which AI is developing put us at a societal crossroads. The choices we’re making now will determine who has access to AI and the opportunity it affords. There is perhaps no more important tool than our education systems for addressing the uneven diffusion of AI we’re seeing currently: national adoption runs above 70% in countries such as the United Arab Emirates and falls to low single digits across much of sub-Saharan Africa, close to a tenfold difference.
The way we teach AI and learn with it will have long-lasting impacts on jobs, the workforce, and our broader economy. Learning Curves: Rethinking Education from Classrooms to Careers in an AI-Driven World is a collection of research-driven essays that can help guide those choices. This book brings together the body of work from the second cohort of Senior Fellows from the Microsoft AI Economy Institute (AIEI), a flagship think tank that produces research and convenes experts to generate insights into how AI is reshaping work, education, and the broader economy.
In Learning Curves, researchers consider how AI is reshaping the earliest stages of learning and work, from K–12 classrooms and community and technical colleges to college‑to‑work transitions and entry‑level jobs, and what it will take for that transformation to widen opportunity rather than deepen divides across institutions, communities, and countries.
Below, I share a few of my own reflections that informed my contributions to Learning Curves, a collaborative effort that wouldn’t have been possible without the truly talented, diverse cohort of researchers who contributed to this book.
Before delving into the specifics of how to incorporate AI in classrooms, we need to ask ourselves whether we’re teaching for knowledge, or for learning.
The book, the calculator, the personal computer, the internet, the search engine, and the smartphone all arrived in classrooms carrying real promise alongside the worry that they would hollow out young minds. Usually the truth landed somewhere in between, and where it landed depended less on the tool than on the choices the adults made about how to use it.
The fact is that the AI-native generation is already here, and its members are in classrooms around the world. More than half of US teenagers already use AI chatbots for schoolwork. I’ve personally seen the promise of AI as a force for good in education up close in my own classroom, where I teach a computer science class to elementary school students.
But of course, the good is only part of the story. We can’t shy away from the downsides of incorporating AI into classrooms. While AI can help with accessibility and individualized learning, it can also create over–reliance, contribute to student cheating, or prevent students from recognizing when information may be false.
The rapid deployment of AI in K–12 classrooms needs to come with a sober assessment of how that technology impacts all students. This assessment must go beyond the technology itself to encompass how students, teachers, parents, and society use AI.
If classrooms are where students learn how to think, teachers are the conduit through which this learning happens. In Learning Curves, Joseph Orero, Senior Researcher in AI at Strathmore University’s School of Computing and Engineering Science, posits that teachers aren’t resistant to AI in classrooms; they just need proper training to help them adequately adapt and teach their students critical skills. His research contribution explores game-based learning with AI-enabled African languages.
AI usage and adoption worldwide are also dependent on accessibility. Another chapter of the book looks at barriers and opportunities in Arabic education and finds that the primary barrier teachers encountered when introducing AI writing assistants into classrooms was Arabic digital literacy, particularly students’ typing ability.
Researchers often circled around a similar theme: there is no one-size-fits-all approach to developing AI competency; instead, such an approach must be sensitive to local knowledge and contextualized teaching and learning practices. This means that teachers, in turn, must have training that makes sense for the unique contexts in which their students are learning.
Frank Nagle, now Microsoft’s Chief AI Economist, Sarah Rodriguez, Associate Professor of Engineering Education at Virginia Tech, and other researchers in Learning Curves make the case that, in many ways, nontraditional pathways such as apprenticeships and two-year-degree programs, particularly those that are virtual, are more nimble and thus better able to adapt their curricula to the rapidly changing field of AI.
Education has never been about simply preparing people for the future of work so they can thrive in the labor market. When considered holistically, it is about teaching students not what to think but how to think. In our AI era, education is one of the core institutions through which the future of work will be built, distributed, and made more (or less) equitable. Successful AI integration remains human-centered by focusing on organizational change, durable human skills like empathy and judgment, strong employer partnerships, and learner needs.
As research from Laura Nuriski, Head of Program on the Future of Work at the Centre for European Policy Studies (CEPS), highlights, access to AI alone does not ensure successful integration. Technology transfer, data infrastructure, organizational readiness, and a human-centered approach to building and deploying AI are also key indicators of success.
Whether we’re talking about the classroom or the boardroom, it’s worth keeping in mind that AI tends to do the most good when it strengthens human capability and the most harm when it substitutes for it. That holds as true in a fourth- grade reading lesson as in a software team or a hospital ward.
The children in our schools deserve tools that serve their learning rather than stand in for it, and that is within our power to provide. Our most successful future with AI will be one that is human-centered, inclusive, and designed to help people thrive.
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Table Of ContentsMore than 3.7 million fraudulent online accounts removed. Twenty-eight arrests across Ghana and Nigeria. More than $11 million in potentially freezable funds identified. And 117 human trafficking victims identified.
These were among the results of Joint Disruption Week, an international operation targeting criminal networks responsible for online fraud, including cryptocurrency investment scams, impersonation schemes, and financial extortion.
“Transnational criminal scam networks operating from West Africa suffered major disruptions last week during a coordinated international ‘Joint Disruption Week,’ an effort that produced arrests, seizures, and the largest-ever takedown of fraudulent social media accounts linked to scam centers in the region.”
Department of Justice
Hosted by the UK’s National Crime Agency (NCA) in London during the week beginning October 6, 2026, Joint Disruption Week brought together investigators from the DOJ, FBI, U.S. Secret Service, Homeland Security Investigations, and international law enforcement agencies, alongside private-sector organizations including Flashpoint.
Throughout the week, participants exchanged real-time intelligence and developed investigative leads to support enforcement actions in Ghana and Nigeria, as well as the disruption of online accounts and infrastructure used by scam networks.
The operation targeted criminal organizations whose activities extend across borders and online platforms. According to the DOJ, scam centers operating in West Africa share characteristics with those previously identified in Southeast Asia, including organized recruitment, training in social engineering techniques, and coordinated fraud campaigns targeting victims internationally. Some operations also involve human trafficking, with individuals coerced into participating in fraudulent activities.
By bringing together law enforcement and private-sector capabilities, Joint Disruption Week targeted both the infrastructure used to perpetrate fraud and the individuals behind these schemes.
The coordinated effort produced results across criminal investigations, financial intelligence, and online infrastructure disruption.
According to the DOJ:
The figures illustrate the reach of these operations. Millions of accounts can support criminal activity across multiple platforms, while the individuals directing those activities may be operating thousands of miles from their intended victims.
The arrests and account removals represent different aspects of the same challenge: identifying the individuals behind criminal operations while disrupting the infrastructure they use to reach victims.
Online scam networks can operate across social media platforms, messaging services, and financial systems, with evidence of criminal activity spread across multiple organizations and jurisdictions. Investigations often depend on connecting information from these different sources to identify the actors involved and the infrastructure supporting their operations.
During Joint Disruption Week, private-sector partners contributed by removing fraudulent accounts, identifying potentially recoverable funds, and sharing investigative leads with law enforcement.
Flashpoint was among the organizations specifically recognized by the DOJ for participating in the operation, alongside Meta, Microsoft, Google, OpenAI, Coinbase, Binance, Chainalysis, Elliptic, TRM Labs, and others. For threat intelligence teams, operations like this highlight the value of identifying criminal actors, understanding how their operations are structured, and connecting activity across disparate sources to support investigative action.
“These scam networks operate like businesses, investing in infrastructure, recruiting people, and developing increasingly sophisticated ways to defraud victims at scale. To meaningfully disrupt them, we have to go after the economics of their operations, making it more costly and difficult to recruit, reach victims, and move illicit funds. The more pressure we put on those operations, the harder it becomes for the criminals behind them to sustain and expand their activities.”
Josh LefkowitzCEO, Flashpoint
Together, the arrests, account removals, and financial investigations demonstrate how coordinated efforts can target multiple components of criminal operations, from the infrastructure used to perpetrate fraud to the individuals responsible.
Joint Disruption Week marked the first coordinated international operation focused specifically on West African scam networks, according to the DOJ.
The arrests, infrastructure disruptions, and investigative leads generated during the operation represent progress against criminal organizations responsible for large-scale financial fraud and human exploitation.
Flashpoint continues to support efforts to identify and disrupt criminal activity through primary source intelligence that helps investigators understand emerging threats and the networks behind them.
For additional information on the operation and its results, read the full Department of Justice announcement.
The post Inside Joint Disruption Week: 28 Arrests and 3.7 Million Scam Accounts Removed appeared first on Flashpoint.
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.
The post What the Nuclear Treaty Got Right, and Why AI Still Can’t Use It appeared first on CISO MAG | Cyber Security Magazine.
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:
Governments, regulators, and standards bodies can request assistance at adg@eccouncil.org
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
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: press@eccouncil.org
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.
The post 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 appeared first on CISO MAG | Cyber Security Magazine.
Table Of ContentsThreat intelligence teams are heading into 2027 with more than ever to monitor, driven by an acceleration in the AI threat landscape. However, their expanded mandate typically doesn’t come with a proportional budget windfall. Instead, teams are under pressure to get greater value from the intelligence, systems, and analyst expertise already available to them.
New independent research commissioned by Flashpoint shows where that pressure is being felt most. Alert noise and false positives are the most frequently cited challenges affecting threat intelligence program effectiveness, followed by analyst bandwidth, changes in the threat landscape, and integration across security tools. At the same time, AI-related threats have emerged as the leading area where practitioners want to expand intelligence coverage.
These findings point to some of the decisions that will shape threat intelligence programs in the year ahead: where to focus finite analyst capacity, how intelligence requirements need to evolve, and where AI can meaningfully support the work. Organizations are already evaluating agentic AI across threat intelligence workflows, but the research also shows that greater autonomy comes with higher expectations around accuracy, control, and measurable value.
For security leaders planning their 2027 threat intelligence strategy and budgets, this report examines:
Together, the findings provide a practical benchmark for teams evaluating their own intelligence operations: where to focus limited analyst time, where automation may have the greatest impact, and what to measure as AI becomes part of the workflow.
As threat intelligence teams plan for 2027, the research shows them managing two simultaneous pressures: intelligence requirements are expanding, while the capacity available to address them remains constrained. AI is intensifying that pressure as adversaries adopt new capabilities, but also becoming part of how organizations are thinking about scaling their ability to defend their organizations.
“2027 will test whether threat intelligence programs can scale their reach as fast as AI is reshaping the threat landscape. The opportunity for agentic AI on the defensive side is significant, and our research shows security leaders are ready to invest. But they aren’t swayed by the hype. They’re holding agentic AI to a high bar, and they want to know where it improves their work, whether they can trust the output, and where human judgment must remain part of the process.”
Josh Lefkowitz
Co-Founder and CEO, Flashpoint
Four findings stand out:
Taken together, the findings show where threat intelligence teams are feeling the greatest pressure, where they see opportunities to extend their capabilities with AI, and what will determine whether those tools earn a lasting place in the intelligence workflow. The sections that follow examine each of these findings in greater detail and what they mean for threat intelligence programs.
The rising AI threat landscape is increasing pressure on organizations to get more capacity from the people and systems they already have. Threat intelligence leaders therefore say the biggest barriers to program effectiveness are operational: too much noise, limited analyst bandwidth, and disconnected tools.
Alert noise and false positives emerged as the leading challenge affecting threat intelligence programs, cited by 65% of respondents. Analyst bandwidth followed at 55%, while half of respondents pointed to both the evolving threat landscape and integration gaps across their security tools.
The impact of these challenges becomes clearer at the workflow level. One healthcare practitioner described receiving alerts about email breaches that were seven or eight years old, with the same data resurfacing each time it was resold and requiring another investigation. Practitioners also described manual steps between intelligence feeds, SIEMs, SOAR platforms, ticketing systems, and response workflows.
Each unnecessary investigation or manual handoff consumes time that could otherwise go toward analysis. That becomes difficult to solve through hiring alone because threat intelligence expertise takes time to develop. As one healthcare threat intelligence leader explained:
“Good analysts take time to develop and train, and we can’t hire our way out of that problem.”
The research suggests an opportunity to look for capacity within the intelligence workflow itself. Reducing repetitive investigations, improving integration between systems, and helping analysts identify relevant intelligence faster can return more of their time to work that depends on context and judgment.
The rise of AI-driven threats is driving increases in the intelligence budgets of many organizations. Among respondents who provided a budget outlook, 50% expect their threat intelligence budgets to increase over the next 12 months, while the remaining 40% expect budgets to stay essentially flat. None anticipate a decrease.
More than a third (35%) expect meaningful growth of at least 10%, suggesting that for many organizations, threat intelligence remains an area of active investment heading into 2027.
Where organizations are increasing investment is equally important. Respondents expecting meaningful growth described spending on AI defense capabilities, expanded threat intelligence feeds and modules, and broader program development. AI-related threat coverage also emerged across both flat and growing budgets as the most frequently cited area where teams want to expand coverage.
For teams heading into 2027, the findings suggest that the question is increasingly where to direct threat intelligence investment for the greatest impact. Organizations are evaluating how existing resources can address new intelligence requirements, where additional capabilities are needed, and which investments can demonstrate clear operational value.
As organizations decide where to focus threat intelligence investment in the year ahead, AI is the clear priority. AI-related threats were the most frequently cited area where organizations want to expand intelligence coverage, including among teams with relatively flat budgets. Practitioners specifically pointed to deepfake detection, AI-accelerated vulnerability exploitation, and emerging AI-enabled threat vectors.
These priorities reflect activity already visible across the threat landscape. During the first half of 2026, Flashpoint tracked more than 22 million threat actor posts discussing, sharing, or advertising AI toolkits for criminal deployment. Flashpoint analysts observed AI being used across phishing, malware development, evasion, and vulnerability exploitation.
For threat intelligence teams, expanding AI coverage requires more specificity than treating “AI threats” as a single monitoring category. Deepfakes may matter because of executive impersonation or fraud. AI-accelerated exploitation may change vulnerability monitoring and prioritization. New attack tooling may affect the adversaries or TTPs a team already tracks.
The challenge extends beyond visibility into these threats. Organizations also need the expertise to understand how AI is changing adversary behavior and attack chains. That expertise is still developing. As one practitioner put it, “everybody’s learning it at the same time.”
Connecting those developments to existing PIRs and business risks gives analysts clearer collection priorities and helps teams determine what new sources, expertise, and monitoring they actually need.
Those requirements are also expanding at a time when analyst bandwidth is already constrained, creating a second question for threat intelligence leaders: where can AI support the intelligence workflow itself?
Nearly half of the organizations interviewed are already using or piloting agentic AI in threat intelligence workflows, while another 35% are planning adoption within the next 12 to 24 months. That makes agentic AI an important part of 2027 planning, but despite the attention surrounding it, threat intelligence teams are taking a measured approach. They are looking for clear business value, evidence that AI can maintain the quality of their intelligence workflows, and appropriate human oversight as agents take on more consequential tasks.
The work being handed to agents already varies considerably, from information retrieval and alert prioritization to more active tasks such as blocking IP addresses, updating blacklists, and monitoring suspicious traffic. Organizations in the pilot stage also described use cases spanning detection, validation, context generation, SIEM alert analysis, TIP querying, MITRE ATT&CK mapping, and reversible response actions.
As agents move from retrieving and analyzing information to taking actions that can directly affect security operations, accuracy and trust become increasingly important. One respondent captured the concern succinctly:
“An agent that’s confidently wrong is worse than one that’s slow.”
That concern is reflected across the research. Practitioners raised questions around data leakage, auditability, regulatory requirements, cost, and the level of control agents should have over security workflows. One retail respondent, for example, would only consider read-only AI assistants because of the risks associated with granting agents write permissions.
For teams evaluating agentic AI, that makes the workflow itself an important part of the decision. Tasks with reviewable outputs, reversible actions, and clear success criteria provide a practical starting point. As autonomy increases, teams need equally clear controls around what an agent can access, what it can change, where human approval is required, and how its actions are audited.
As threat intelligence leaders plan for 2027, the research points to a clear set of priorities. Teams have more to monitor, limited analyst capacity, and growing interest in AI as a way to scale the work their programs already perform.
The findings also show that practitioners are selective about where they see that value. PIR coverage monitoring and intelligence briefings can extend coverage and reduce recurring work while keeping analysts involved in the intelligence process. As agents take on more consequential actions, questions around accuracy, permissions, auditability, and human oversight become more important.
For teams setting priorities for the year ahead, that creates a practical path forward: understand where analyst capacity is being consumed, connect new intelligence requirements to the risks that matter to the organization, identify workflows where automation can meaningfully extend the team, and establish clear measures of success before deployment.
The challenge for 2027 will be turning expanding intelligence requirements into an operating model that can scale for threat intelligence programs. That means making better use of existing data, systems, and analyst expertise while being deliberate about where agentic AI can extend those capabilities and where human judgment remains essential.
Flashpoint commissioned independent qualitative research among a select group of more than 20 threat intelligence budget owners and influencers at large North American enterprises across financial services and insurance, healthcare and biotech, retail, and information technology and telecommunications during the early fall of 2026.
The post The Enterprise Threat Intelligence Report: 2026 appeared first on Flashpoint.
Table Of ContentsFirst discovered in April 2026, CastleStealer is a C#-based information-stealing malware that has continued to add new capabilities since its emergence. Newer samples analyzed by Flashpoint can bypass app-bound encryption in Chromium-based browsers, execute commands remotely, and exfiltrate stolen information in small encrypted transmissions rather than a single large archive.
While Flashpoint has not yet identified a large influx of threat actors using CastleStealer, its continued development makes it an emerging threat worth watching.
CastleStealer was first publicly identified in April 2026, when it was observed using ClickFix social engineering to deliver a Python script that executed a loader referred to as “CastleLoader.”
By June, the malware had appeared in a different distribution campaign. Threat actors used malicious advertisements to direct victims to fake Node.js installation websites, where a batch script masquerading as an installer ultimately downloaded and executed a newer loader known as OXLOADER.
OXLOADER employs several techniques designed to make analysis and detection more difficult, including multiple rounds of self-decryption, obfuscated API resolution, sandbox checks, and in-memory execution. Flashpoint analysts assess that the loaders used to deliver CastleStealer appear to be developed in-house and demonstrate significant technical ability around anti-analysis and stealth.
But the delivery chain is only one part of CastleStealer’s evolution. Newer samples show that the infostealer itself has continued to expand what it can collect and what threat actors can do after a system is compromised.
Once executed, CastleStealer first checks the system’s Multilingual User Interface (MUI) languages for Russian (ru-RU). It then connects to its command-and-control (C2) infrastructure and sends a handshake packet containing its build UUID and basic information about the infected host. After establishing the connection, it gathers additional information about the victim machine and sends it to the C2.
From there, CastleStealer begins searching for sensitive information across browsers, applications, and files on the victim’s system.
For Chromium-based browsers, CastleStealer collects login data, cookies, browsing history, web data, and information stored by browser extensions. That includes extension IDs, IndexedDB data, and extension storage.
The malware also targets:
That browser targeting has also become more capable since CastleStealer was first discovered.
One of the clearest examples of CastleStealer’s evolution is its ability to target data protected by newer browser security controls.
Earlier analysis found that CastleStealer could not steal data from updated browsers using app-bound encryption. Newer samples analyzed by Flashpoint now use Chrome’s IElevator COM interface to bypass that protection, a technique also used by other modern infostealers.
The change closes a meaningful limitation present in earlier CastleStealer samples and expands the browser data available to the malware.
Newer CastleStealer samples also give threat actors capabilities beyond stealing information from an infected machine.
CastleStealer contains basic remote shell functionality that allows an operator to send a shell command to the victim system, provide a file for execution, or send the URL of another payload that CastleStealer will download and execute.
That capability gives an attacker additional options after the initial compromise. Instead of ending the operation once targeted information has been collected, an operator can interact with the infected system and introduce additional payloads.
CastleStealer also differs from many other infostealers in how it sends stolen information back to its operators.
Rather than collecting stolen data into an archive and transmitting it to a C2 server or Telegram channel, CastleStealer sends smaller amounts of collected data over raw TCP. The transmissions are encrypted using AES.
Flashpoint analysts assess that transmitting data this way may help CastleStealer avoid network detections associated with a single spike caused by a large data transfer.
The network packets follow a straightforward structure:
{4-byte size} → {AES IV} → {Encrypted Data}
Once CastleStealer completes its activity, it deletes itself using a common ping-delay self-deletion technique.
CastleStealer’s development since its discovery illustrates how quickly an emerging infostealer can mature. Its operators have changed how the malware reaches victims, while the stealer itself has gained the ability to bypass browser protections, remotely execute commands and additional payloads, and exfiltrate stolen data in smaller encrypted transmissions.
CastleStealer has not yet seen the widespread adoption associated with more established infostealer families. But its trajectory makes it one to watch as its developers continue to improve both its collection capabilities and what operators can do after compromising a victim.
Flashpoint continuously monitors emerging malware, illicit communities, and threat actor infrastructure to identify these shifts as they happen. Request a demo to learn how Flashpoint’s primary source intelligence and analyst research help security teams identify emerging threats and respond before they become widespread.
The post CastleStealer: An Emerging Infostealer Growing More Sophisticated appeared first on Flashpoint.
Table Of ContentsIn today’s threat landscape, speed and actionable context are critical. When a high-priority threat emerges, pairing best-in-class threat intelligence data with hyper-responsive human expertise ensures organizations can act immediately and with confidence. To turn raw threat data into actionable defense, Flashpoint unites unmatched primary source collection, cutting-edge AI features, and over 100 analysts fluent in more than 35 languages—ensuring organizations receive localized context, rapid analysis, and long-lasting strategic value.
Flashpoint Customer Success & Support (CS) consistently delivers industry-leading responsiveness and resolution speed. Driven by strict internal Service Level Objectives (SLOs), our metrics showcase our commitment to our customer’s operational success:
*CSAT and Time to Solve figures are based on real customer inputs from January 1, 2026 to September 2026.
I work with 16 vendors and Flashpoint support is the best by far.
VP of Threat Intelligence Data & EngineeringMajor Financial Services Client
This commitment to responsiveness is why we were honored to recently be named a Customer Favorite in The Forrester Wave: External Threat Intelligence Service Providers, Q3 2026 report, where Forrester specifically noted that “customers laud Flashpoint’s white-glove approach to addressing diverse customer needs.”
But what does top-level service look like behind the scenes?
Here is a look at how Flashpoint CS operates as an extension of our customers’ security operations.
Unlike traditional CS models that limit you to a single point of contact, customers are supported by Flashpoint’s entire team of Customer Success Managers (CSM) experts. Our team is comprised of former intelligence operators and seasoned practitioners whose collective experience spans decades across critical industries and sectors, including:
Flashpoint customers are paired with a dedicated CSM who can seamlessly draw on our broader team of industry specialists. Flashpoint customers gain speedy, highly knowledgeable insights without bottlenecks. This model provides organizations with timely responses for any urgent specific sector threats, with our CS teams bringing in practitioners who have direct, real-world experience in that field.
“Flashpoint has been instrumental in supporting Threat Intelligence engagements.
Major Finance and Risk Firm
We’ve seen a significant increase in ransomware-related data exposure cases, and Flashpoint’s responsiveness and quick turnaround times have been invaluable in helping us address these matters. We appreciate the partnership and support the CS team has provided.”
In addition to traditional support, Flashpoint’s CS team also support clients with:
“Thanks to the team for all of the support throughout the evaluation and onboarding process. Everyone was responsive, engaged, and worked closely with us to ensure we had the information and guidance needed to make informed decisions and get the platform operational quickly.”
Major Financial Services Client
Flashpoint is a customer-first intelligence provider; we turn customer feedback into action. Our CS teams serve as the direct voice of the customer, taking feature requests and quality-of-life updates to Flashpoint Ignite product managers and engineers to help shape product roadmaps.
Real-world examples of how customer feedback directly drives platform enhancements include:
Powerful intelligence technology is only half the equation—unmatched operational support is what drives long-term success. Customer feedback reinforces our belief that Flashpoint’s recognition as a Forrester “Customer Favorite” mirrors our commitment to pairing world-class threat data with exceptional service. Request a demo and see how the combination of best-in-class data and unparalleled customer service strengthens your security operations to maximize your threat intelligence investment.
Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. This report is part of a broader collection of Forrester resources, including interactive models, frameworks, tools, data, and access to analyst guidance. For more information, read about Forrester’s objectivity here.
The post Beyond Support: How Flashpoint Redefines the Customer Success Experience appeared first on Flashpoint.
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
The post Atlanta Set to Host Hacker Halted and Global CISO Forum 2026, Uniting Practitioners and C-Suite Leaders appeared first on CISO MAG | Cyber Security Magazine.
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