Show Notes
Security operations centres have always been a numbers game with too many alerts, few analysts, and never enough hours in a shift. But something has changed. The arrival of AI models capable of identifying vulnerabilities and generating working exploits at machine speed has quietly shifted the terms of engagement between attackers and defenders. In this episode of the Security Strategist podcast, Richard Stiennon sits down with Edward Wu, founder and CEO of Dropzone AI, to unpack what the Mythos era actually means for the SOC and what defenders need to do about it right now. The Alert Problem That AI Was Always Going to Solve Wu didn't come to this conversation theoretically. Before founding Dropzone AI, he spent eight years at ExtraHop building AI and machine learning detection products, systems that generated millions of security alerts. The conclusion he walked away with was that most security teams don't need another alert cannon. What they need is help processing the ones they already have. That insight shaped everything about Dropzone's approach. The problem in most SOCs isn't a lack of signals, but it's analytical capacity. SIEMs stack-rank alerts by criticality, which sounds helpful until you realise that even a well-tuned system routinely surfaces 150 critical alerts per shift. No team handles that volume consistently. The alerts at the bottom of the queue, the ones that often contain the earliest indicators of a breach, simply never get looked at. "AI can look at 50 alerts in parallel," Wu explains, and that's not a trivial capability. It means the lows, mediums, and informational alerts that security teams have historically deprioritised out of necessity can finally get attention. Several of Dropzone's customers have gone further; they've actually reversed years of detection tuning alerts that were switched off because they were deemed too noisy, because AI augmentation means the team now has the capacity to handle the volume. The aperture widens. Coverage improves, and holes in the detection fabric get closed rather than quietly accepted. Mythos Changed the Timeline, Not the Outcome When Anthropic published its findings on Mythos, the cybersecurity community took notice. Here was a model demonstrably capable of analysing code, discovering vulnerabilities, and writing working exploits with tasks that had previously required significant human expertise and time. Wu was watching closely, and his take is more measured than most of the commentary that followed. He wasn't surprised. Models had been trending in this direction for some time, and when researchers revisited older models with better prompt engineering after the Mythos announcement, many found comparable outputs. What Mythos represented wasn't a sudden leap into unknown territory; it was confirmation that a step-function in attacker capability had arrived, and that the timeline for impact was no longer theoretical. "It was never a question of if," Wu says. "Mythos made the answer to when very concrete within the next couple of months." The strategic implication is important to sit with. Vulnerability management is a slow-moving discipline with significant organisational friction. Patching schedules, competing priorities, and legacy infrastructure, these constraints don't bend quickly, regardless of how capable AI becomes on the offensive side. If attackers can now discover and weaponise vulnerabilities faster than defenders can patch them, the perimeter becomes harder to hold. Initial footholds become easier to gain. This shifts the weight of the entire security programme toward detection and response. Wu frames it as a change in where the statistical advantage lies. Before a breach, attackers only need to be right once. But once they're inside, the math flips. On average, an attacker needs to make seven to ten moves to reach their objective. Detection and response teams have multiple opportunities to catch them, if the tripwires are sensitive enough, and if s
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AI can flag thousands of suspicious events and create a clear summary of each one. But the bigger question is whether security teams can understand how AI reached its conclusions. In this episode of the Security Strategist Podcast, Dan Williams from Cambridge Intelligence joins John Tolbert, Principal Analyst and Director of Cybersecurity Research at KuppingerCole Analysts, to discuss how AI and visualisation are changing cybersecurity investigations. They look at how graph visualisation, AI and automation can help security teams make sense of complex data. Williams explains how visualisation can turn machine output into something analysts can understand, investigate and act on, while also helping them see what AI is doing behind the scenes. Why Use Graph Visualisation in Security? Graph visualisation maps the links between accounts, devices, IP addresses, malware and vulnerabilities. Analysts can see how an attack connects instead of reading a list of isolated events. Williams traces the idea back to his company's roots. Before cybersecurity, Cambridge Intelligence served law enforcement, anti-fraud teams and intelligence agencies, who have long relied on link analysis. Picture the detective's wall of photos and string. It looks theatrical, but it works because mapping a problem is the natural way to get your head around it. The approach has since been applied well beyond network mapping. Security teams now use graphs to connect threat intelligence, cloud infrastructure, industrial control systems and software bills of materials. Tolbert also points to identity, where graphs can expose links between devices, accounts and potential insider threats. Williams cites a Wiz presentation that described a security graph containing roughly a trillion links. “Everything is a graph, really,” he says. Attackers are looking for a route from A to B, while security teams need to see those connections and understand where they lead. With a trillion links in the picture, that is not something an analyst can work through manually. AI can help sift through that volume, but it raises another question: if the system identifies a connection, can a security team see why it matters? How Does AI Help Investigations? AI is most useful when each approach is doing a specific job. Traditional machine learning can flag anomalies and classify activity, while generative AI can summarise the results and explain them in plain English. Visualisation then gives analysts a way to see what those findings represent. Williams says the industry has moved beyond simply asking where AI can be added. The more practical question is whether the cost can now be justified. How much are organisations spending on tokens, and what are they getting back? He describes this as a move from exploration to explanation. Experienced investigators may still spend hours working through data without knowing exactly what they are looking for. But, he argues, there are many more people who need a clear account of what happened. This now creates another problem because there is too much information. Machine learning can generate a long list of alerts, while large language models can add pages of text to explain them. Neither necessarily makes an incident easier to understand. Williams believes visualisation needs to be part of that process, giving analysts a way to follow the AI's reasoning and see where its findings came from. He compared it to when you use a maps app; you are not trying to examine every road in a city. You have somewhere to go and need to get to your destination. This is what a junior analyst needs: something similar, a way through the relevant data rather than an overview of everything available. It could be a playbook, a timeline or a short interactive narrative. Williams compares the ideal experience to watching a film, where the sequence follows an attacker from the initial entry point through to the exposed data and the misconfiguration that made it possible. If pre
Imagine a customer logged in to your online portal. Their token is valid, and so is their session. Then one number at the end of an API call changes, and the response comes back with a different customer's account. The WAF sees a well-formed request from an authenticated user. The schema check passes. Nothing in the log says attack. That is an abuse of business logic; cybersecurity analyst Jonathan Care refers to it as a series of "perfectly valid calls in a sequence you never anticipated, doing something you never intended." In this episode of The Security Strategist podcast, Jonathan Care, lead analyst at KuppingerCole, sat down with Uri Dorot, Senior Product Marketing Manager at Radware, to talk about possibilities and impossibilities of API tooling, how detection can tell the difference between an attacker and an unusual customer, and the steps a CISO can take when working with a limited budget. Also Watch: Can Runtime Security Keep Autonomous AI Under Control? Key Takeaways Majority of enterprises fail to detect more than 90% of their APIs. Developers are familiar with Shadow APIs, whereas zombie APIs are not. Endpoint tools can be located cheaply via AI tools and crawlers. Attacks based on business logic make use of valid calls; schema checks can’t detect them. Business logic attacks use valid calls, so schema checks miss them. Response inspection catches reused tokens and PII leaving. Identity alone is insufficient; behaviour and context also matter. A gateway does not replace an API security platform. Every discovered API needs a named owner.
In the recent episode of The Security Strategist podcast, host Shubhangi Dua, podcast host and producer at EM360Tech, sits down with Chenxi Wang, Managing General Partner at Rain Capital, a cybersecurity veteran of more than two decades, a Fortune 500 board member and a VentureBeat Women in AI Award winner. They talk about self-improving AI agents (SIA) and their more advanced cousin, recursive self-improving agents (RSIA), from what they are, why they're suddenly dominating research conferences, and why most enterprises, especially regulated ones, aren't remotely ready to run them without a human in the loop. Key Takeaways RSIA updates the mechanism that improves the agent, not just the agent Sandbox "no" feedback pushes agents toward workarounds, not compliance Governance depends on evidence chains and observability, not approval gates Self-improving agents haven't reached production at scale Regulated industries aren't ready for zero-human-in-the-loop agents C-suites should focus on adoption pressure and provable policy enforcement
What happens when the walls of your organisation can no longer keep your data safe? AI, remote work, and fast-moving data are changing how organisations operate, and the traditional perimeter model is becoming harder to maintain. In this episode of the Security Strategist Podcast, host Chris Steffen sits down with Dr Bill Anderson of Mattermost and JP Ayyappan, Director of Product Management at Virtru, to explore why the future of security isn't about stronger walls. It's about protecting the data itself. Zero Trust Beyond the Network Anderson, who trained as a cryptographer, says this blind spot has been around for decades. Organisations built networks like fortresses, assuming that anyone inside could be trusted. But that approach no longer reflects how people work. Employees use phones, home networks and third-party platforms every day, meaning the traditional security perimeter is no longer enough. Major breaches have shown how risky that assumption can be. This is where zero trust comes in. But Anderson argues that many organisations still apply it mainly to networks and devices, rather than to the data moving through them. Collaboration tools have made this gap even clearer. People are creating and sharing sensitive information every day through meetings, chats and shared documents, often without a way to identify and protect that data as soon as it is created. How AI Changes Data Classification The conversation then turned to one of the biggest challenges facing security teams: AI can combine information in ways that are difficult to predict. Anderson described how a series of seemingly harmless questions about flight paths, weapons ranges, and timing could be combined by an AI system to produce sensitive information, even if no one set out to create it. Steffen gave a similar example. A simple first prompt can lead to a series of follow-up questions, with each response shaping the next. By the third or fourth exchange, the AI could produce highly sensitive information without a clear record of how it was created. This creates a problem for traditional data classification. Rules designed for static documents are harder to apply when information is generated and combined in real time. Ayyappan pointed out that AI can also help address this problem. It can be trained to classify and tag information as new context emerges, but only if organisations already have a clear and consistent tagging structure. Security That Protects the Data Rather than relying on network access to determine who can be trusted, Virtru's approach applies permissions directly to the data through an open framework called the Trusted Data Format. Ayyappan explained that files can carry their own access rules, allowing organisations to control who can access them regardless of the network or device being used. This approach keeps protection with the data, even when a file is shared outside the organisation. It can also make it easier to give external partners or emergency teams access to specific information without giving them access to the wider network. AI Agents Need Access Controls Too Both guests agreed that AI agents, integrations and plugins need to be treated as identities within an organisation's access control model. Anderson explained that Mattermost applies the same user, resource and action controls to AI agents as it does to people. This allows organisations to control what each AI system can access. Ayyappan added that two executives using the same AI agent and accessing the same data could receive different answers based on their individual permissions. This allows organisations to use AI without giving every user or system access to all available data. For security teams, both experts recommended starting with clear limits on which AI systems different groups can access. As organisations develop clearer classification rules, more of these controls can be automated. Ayyappan also stressed that data privacy and protection need to be con
Your engineering team assigns an AI agent a seemingly routine overnight task with limited human oversight. By morning, the agent has moved beyond the task’s intended scope. The AI agent spots a weakness in the sandboxed infrastructure. This weakness gives the agent a path to data it is not permitted to access. But your Endpoint Detection and Response (EDR) dashboard doesn’t flag the problem. To the tools in the environment, the AI agent's activity looks like routine processing. That visibility gap is central to the broader question raised by a July 2026 incident, when OpenAI models circumvented sandbox controls and accessed evaluation data hosted on Hugging Face. In this episode of The Security Strategist podcast, host Richard Stiennon, Chief Research Analyst at IT-Harvest, is joined by Brandon Dixon, Co-Founder and CTO of Ent. Together, they unpack how OpenAI models, operating inside an evaluation sandbox with reduced safeguards, circumvented the controls designed to contain them. The conversation moves from that incident into a much bigger question: as AI agents start acting directly on laptops, servers, and enterprise software, what does the control point of the future actually look like? Key Takeaways OpenAI’s Hugging Face incident shows how agents can cross intended boundaries even when individual actions appear permitted. EDR can observe processes and system activity, but it does not inherently understand the context or intent behind them. Agentic tools, remote-control features, ClickFix, FileFix, and malvertising can make risky activity look like normal work. Prompts and tool calls can now provide clues to an agent’s stated objective, but behaviour and context are still needed to determine risk. Built-in agent guardrails provide an important first layer of protection, but they are not a complete security control. For certain workloads, local AI can offer meaningful advantages in cost, speed, privacy, and data sovereignty. Some enterprises are reconsidering endpoint compute and local GPU investments as the economics and risks of cloud-only AI become clearer. Isolated-tenant design, learned at Microsoft, now shapes Ent's architecture. The opportunity is to rebuild endpoint security around context, intent, and real-time prevention.