The Security Strategist

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.

Chapters
00:00 Introduction to AI's impact on cybersecurity
00:27 Brandon Dixon introduces Ent and recent AI incidents
01:09 What happened with Hugging Face and AI security risks
02:19 AI models, guardrails, and the risk of AI escaping sandbox environments
03:28 Deciphering AI intent through prompts and behaviour analysis
04:45 Monitoring AI activity and understanding agent behaviour
05:52 The future of AI and human roles in security
07:16 Limitations of current endpoint security solutions
09:15 The resurgence of endpoint devices and local compute power
13:46 Economic and practical reasons for local AI processing
15:01 Leveraging latent endpoint compute for security and AI tasks
16:12 The architecture shift in cybersecurity and point solutions
17:37 The debate over cloud versus on-premises security infrastructure
20:14 The role of hardware and local compute in AI security
22:36 Limitations and use cases for current AI security solutions
23:36 Future security architecture and the role of AI in security design
25:04 Key takeaways for security leaders and the importance of rethinking architecture
26:23 Brandon Dixon on building a new security architecture for the future

Request a demo to learn more about what Ent is building and how its intent-aware workspace security platform helps security teams understand human and AI-driven activity at the endpoint, recognise risky behaviour that traditional tools may miss, and intervene before it becomes an incident.
#AIsecurity #EndpointSecurity #Cybersecurity #AIagents #TheSecurityStrategist

Every CISO’s inbox is hot with this email at least once a year: the audit is four months away, and it's time to stop everything.
As a result, progress comes to a halt, and a person then spends two weeks logging into 30 different consoles, taking screenshots in order to show that MFA has been turned on. After the audit is over, the evidence becomes invalid, and exactly twelve months later the same frantic situation begins all over again.
That is precisely the kind of situation that Jonathan Schipp, Senior Director of Product Management at Rapid7, aims to end, and it is the focus of the most recent episode of The Security Strategist podcast.
In this episode, host Richard Stiennon, Chief Research Analyst at IT-Harvest, is joined by Schipp to talk about the reason why governance, risk and compliance (GRC) is moving from being a yearly rush to becoming a continuous, API-driven element of security operations. They also address why AI is causing this change to happen more quickly than most GRC teams can keep up with.
“Compliance turns into something you have to put everything else aside for,” Schipp tells Stiennon. “When an enterprise carries out its SOC 2 audit, most of them are not well prepared; it disrupts the business flow.”
Key Takeaways
Rapid7 launched CyberGRC to connect live security telemetry to compliance evidence
Continuous monitoring replaces manual screenshot evidence with API-based checks
SOC 2 and ISO 27001 still require annual audits; continuous monitoring closes the gaps between them
Roughly 1% of discovered vulnerabilities are actually exploitable, per Schipp
AI models are finding more vulnerabilities mainly by scanning source code faster
Automated exploitation attempts generate high log volume, making them detectable
Rapid7's GRC platform supports ISO/IEC 42001 and the NIST AI RMF
Rapid7 serves roughly 11,000 customers, many now requesting AI-specific controls
GRC's role is to move the business through AI risk, not block AI adoption
Boards typically have audit and risk committees but no dedicated security committee
Vulnerability risk should be classified by business impact, not treated as uniform
Basic controls — MFA, asset inventory, patching exploitable exposures — stop most attacks, AI-driven or not
Chapters
00:00 Introduction and guest overview
02:07 Incentives for security maturity
03:06 European and US regulatory developments
03:50 Connecting security telemetry with GRC
04:16 Continuous compliance and audit cycles
05:16 The importance of continuous control monitoring
06:20 Using APIs for evidence collection
07:16 Managing vulnerabilities and exceptions
08:18 Classifying vulnerabilities and risk
09:15 AI in security and human accountability
09:53 The reality of AI discovering vulnerabilities
11:08 Managing noise and false positives in AI detection
12:10 Integrating AI systems for proactive security
13:26 Preemptive security and threat intelligence
14:17 Risks of AI in attack scenarios
15:16 AI misuse and governance challenges
16:23 Balancing AI adoption with controls
17:22 The importance of basic security controls
18:22 Key takeaways for CISOs and security leaders
20:19 Closing remarks and next steps
For further information, visit rapid7.com and em360tech.com.

Security teams are drowning in telemetry, and the tools built to make sense of it were designed for a world that no longer exists. On a recent episode of the Security Strategist Podcast, host Richard Stiennon sat down with Cliff Crosland, co-founder and CEO of Scanner, to unpack why traditional SIEM platforms are buckling under modern log volumes. Together they also covered how AI agents are starting to change what's possible in threat detection, response, and hunting.
Crosland’s journey into cybersecurity didn’t follow the usual path. He and his co-founder spent years as backend and distributed-systems engineers at earlier startups, where they ended up running the SIEM alongside everything else. Those experiences exposed a clear gap, which eventually became the foundation for Scanner.
Why Traditional SIEMs Struggle With Modern Log Volumes
Crosland traced the SIEM’s roots back to on-premise platforms such as QRadar, ArcSight and Splunk. They were built for an era when security data was measured in gigabytes per day. Today, cloud infrastructure, sprawling SaaS environments and autonomous agents can generate terabytes or even hundreds of terabytes of data. That architecture mismatch forces security teams into a painful trade-off: either get selective about which logs even make it into the SIEM, or watch performance and cost spiral out of control.
The economics get worse as environments grow. Adding servers to keep an aging cluster alive can become more expensive than the software license itself, and searches that once took seconds can stretch into hours once a SIEM is overwhelmed. The result, Crosland argued, is that most organisations end up retaining far less historical data than they would like, right as AI-driven attacks make that history more valuable, not less.
His prescription is architectural: decouple storage from compute using cloud and object storage, the way modern data infrastructure generally works, rather than forcing everything through a stateful, on-prem-style cluster. That shift, he said, is the only realistic way to make comprehensive log monitoring affordable at scale.
How AI Agents Are Reshaping Threat Detection
The conversation turned to what becomes possible once teams aren't rationing their log history. Crosland described a workflow where agents can pick up fresh threat intelligence - a vendor breach discovered months after the fact - and immediately search years of historical data instead of waiting on a slow, expensive rehydration process. Investigations that once consumed a day or a week can compress into seconds.
That speed feeds what Crosland called a virtuous cycle: threat intelligence drives threat hunts, threat hunts surface gaps, and agents translate those findings into new detection rules, tuned to an organisation's specific environment and back-tested against historical data before a human reviews and ships them. Rather than writing every rule by hand, analysts increasingly guide and validate what agents propose.
Agents also change the calculus around alert volume. Teams have historically suppressed alerts to avoid overwhelming analysts, but with agents handling first-pass triage, Crosland said organisations can afford to monitor a much wider surface area because the initial sorting no longer depends entirely on human bandwidth.
Why Monitoring Everything Matters
Stiennon pointed to another worrying trend: 2026 has already seen a sharp rise in disclosed vulnerabilities. Attackers are increasingly using AI to chain together low-severity flaws that once seemed safe to ignore. Crosland pointed to the widely discussed Hugging Face incident, where AI agents reportedly communicated by encoding data into directory names, as an example of exactly the kind of activity that slips past teams focused only on "obvious" high-severity signals.
The fix, he argued, is to monitor high-volume log sources that teams often cut for cost reasons, including package manager activity, VPC flow logs, and DNS queries. Early signs of a chained exploit can appear as simple anomalies, such as an unexpected spike in log volume. Catching those patterns can mean detecting an incident in minutes rather than months.
Looking ahead, Crosland sees a hybrid model taking shape. A SIEM handles the most critical alerts, while a data lake stores the remaining log sources previously cut for cost reasons. Agents can query both, alongside tools such as CSPM platforms, to build a fuller picture before escalating to a human analyst.
His advice to security leaders is to look for cheaper, more scalable ways to store and search log data before deciding what to cut, and treat AI agents as force multipliers for threat hunting and detection tuning rather than replacements for team judgment. As he put it, the goal is a security program where monitoring "everything" is no longer an aspiration but a baseline expectation.
For more on Scanner’s approach to security data, visit scanner.dev. Or connect with Cliff Crosland on LinkedIn.
Takeaways
Limitations of traditional SIEM systems in handling large log volumes.
The role of AI and data lakes in enhancing threat detection and response.
The importance of monitoring high-volume logs like network traffic and package manager logs.
Using AI agents for threat hunting and detection engineering.
The impact of AI on vulnerability discovery and chaining low-level exploits.
Strategies for security leaders to leverage data lakes and hybrid architectures.

Chapters
00:00 Introduction to Evolving Security Operations
00:32 Challenges with Traditional SIEM Systems
03:00 Limitations of On-Prem SIEM Architectures
04:21 Scaling Log Data with Cloud and Data Lakes
06:21 Impact of AI on Log Volume and Monitoring
07:50 Monitoring Cloud and SaaS Infrastructure Logs
09:02 AI and Threat Hunting in the New Era
10:29 Detection Engineering and AI Agents
14:48 The Future of Threat Detection and Response
21:59 Modern SecOps and Data Lake Integration
25:28 Advice for Security Leaders

There's a particular kind of exhaustion that comes with modern security work. It isn't a shortage of information but actually quite the opposite. Security teams are drowning in it from alerts, logs, AI-generated summaries, and twenty-page reports nobody has time to read properly. So when AI promises to cut through the noise, the obvious question is whether it's actually helping, or just creating more work at a faster pace.
This was the question at the heart of this episode of the Security Strategist Podcast, where host Trisha Pillay sits down with Dan Williams, Chief Product Officer at Cambridge Intelligence, to talk about the role visualisation plays as AI takes on more of the analytical heavy lifting in cybersecurity.
Complex Security Data
Cambridge Intelligence may not be a name most people recognise, but its technology sits behind many of the security products organisations already use. The company’s roots go back around 15 years to the law enforcement and intelligence world, where investigators were dealing with a simple problem of trying to make sense of thousands of connections between people, places, devices and transactions.
The answer was to make those relationships visible. Instead of working through rows of data or pages of reports, investigators could map connections and see patterns that were difficult to spot in a spreadsheet. That approach, known as link analysis or graph visualisation, has since expanded beyond law enforcement into areas such as financial crime, supply chains and cybersecurity. Today, that same principle is being applied to increasingly complex security environments. Teams may have thousands of alerts, cloud assets, identities and activity logs to work through. The challenge is no longer simply collecting the information; it is giving people enough context to understand what matters and what they should do next.
This is where Cambridge Intelligence sees its role. As Williams explains, the company sits between the underlying data and the person trying to make sense of it. This means essentially providing the “last mile” of the data. The technology turns complicated information and relationships into something people can explore visually. By doing this, it helps them move from raw information to understanding and being able to make a decision based on the data seen.
The human layer is still very important as AI takes on more of the work of detecting patterns and raising alerts. AI may be able to identify something unusual, but security teams still need to understand the context behind that finding before they can decide what to do about it. Visualisation can help bridge that gap by showing how an alert connects to the wider environment.
Exploring data to explaining it
In the early days of threat intelligence, graph visualisation gave security teams a way to investigate connections between threat actors, malware, IP addresses and vulnerabilities. Analysts could explore the data themselves and look for patterns. This approach becomes harder to sustain as security data grows. Most cybersecurity professionals aren't specialist threat analysts, and no one is going to manually examine billions of transactions to find a suspicious one. The technology needs to do more of the searching. But finding something isn't enough; security teams also need to understand why this is important. This is the shift Williams describes as moving from “explore” to “explain”. A small group of specialists may still want to investigate the raw data, but most users need the relevant information presented with enough context to make a decision.
Cloud security is a good example of how modern attacks can involve an enormous web of systems, identities and vulnerabilities, creating graphs with potentially billions or even trillions of relationships. The answer isn't to put all of that information on one screen. It is to show the part that matters. Williams compares it to following a route from A to B. The destination isn't enough; you need the important waypoints along the way, essentially a map. In cybersecurity, those might be the systems an attacker could reach, the data that could be exposed or the single vulnerability that could stop the attack from spreading. Good visualisation, then, isn't about showing everything. It's about showing enough to understand what matters.
Giving security teams the bigger picture
Identity and access management creates a similar problem. Security teams are dealing with huge volumes of login activity, API calls and increasingly non-human identities, making raw logs difficult to interpret. Logs can tell you what happened, but they don't always make the relationships between those events obvious. Graph visualisation can add that missing context by showing who has access to what, which groups they belong to and how a change in one part of the environment could affect another. Analysts can still drill into individual events when necessary, but they get the wider picture first.
There is an obvious trade-off, though. Simplify a security environment too much and important context disappears. Show everything and the result becomes another overwhelming dashboard. Williams describes finding that balance as more art than science. The right view depends on the question being asked. A CISO looking for an overall risk picture needs something very different from an analyst investigating a specific incident. The goal isn't to make security data simpler for the sake of it. It's to make complexity easier to navigate.
What should security leaders ask their vendors?
Williams' advice to CISOs is: Can you explain what your security technology is doing? If you can't sketch it out on a whiteboard or explain how it reaches its conclusions, that's worth questioning. Security technology doesn't need to be simple, but the people responsible for it need to understand enough about what's happening behind the scenes to trust it. And that brings the conversation back to the wider role of visualisation. As AI takes on more of the work involved in detecting and analysing threats, security teams need ways to understand those decisions rather than simply accept them. The value of visualisation isn't making security data look better. It's turning complexity into something people can understand and act on.
Takeaways
The role of visualisation in cybersecurity and AI.
The importance of context and storytelling in security data.
How visualisation shifts from investigation to explanation.
Balancing information overload with clarity.
The human element in AI-driven security decisions.

Chapters
00:00 Introduction to visualisation in cybersecurity and AI
01:01 Dan Williams introduces Cambridge Intelligence and its role
02:36 The evolution of visualisation from law enforcement to cybersecurity
03:13 The challenge of maintaining context with increasing data complexity
04:38 AI's role in adding context and reducing noise
05:34 From detective tools to storytelling in security visualisation
07:18 Mapping attack paths and explaining security data
09:55 The shift towards storytelling and narrative in visualisation
10:24 Using visualisation to manage high-volume security data
12:36 Balancing detail and clarity in security visualisation
15:47 The human role in AI-driven security decisions
16:24 Decision gates and autonomous AI actions
18:01 Visual summaries and engaging reports for security teams
20:02 The importance of understandable and trustworthy AI solutions
22:04 What CISOs should demand from vendors
23:34 Closing thoughts and key takeaways

Rolling out artificial intelligence across a business sounds pretty straightforward until security enters the conversation. On a recent episode of the Security Strategist Podcast, host Richard Stiennon sat down with Omar Khawaja, Field Chief Information Security Officer at Databricks, and Danny Healy, the company's Lead Data & AI Strategist, to explore why so many organisations fail when moving AI from pilot to production. Everything always looks good in theory, but it's harder to execute in reality. This discussion offers a practical take on AI governance, risk and the cultural shifts security teams need to make to protect their business at scale.
Why Shadow AI Grows on Indecision
Khawaja opens with a warning that should resonate with any CISO watching AI adoption outpace their controls. The instinct to "wait and figure it out" is, in his view, the single biggest misconception in AI security. Every month spent deliberating is a month in which employees quietly adopt unsanctioned tools themselves, and that delay creates a wider window for shadow AI to spread unchecked.
The bigger issue, he explains, is that security teams keep reaching for playbooks built for deterministic systems, basically software that behaves predictably every time. However, AI doesn't work in this manner. It's probabilistic, which means outcomes are very different, and organisations that expect old-world controls to transfer seamlessly are setting themselves up for failure. This mismatch tends to push companies toward one of two extremes, which is either drowning use cases in exhaustive control lists or quietly ignoring the problem until it becomes unavoidable.
Khawaja’s solution is straightforward, despite the complexity of the problem. Before writing a single policy, he asks leadership teams a question: can your architects actually draw what your AI system looks like? Without a shared view of the main components, which range from data pipelines and models to agents and permissions, different governance teams can end up solving different problems, all while thinking they are on the same page when they are not.
He also takes a pragmatic view of AI adoption. Drawing on Thomas Aquinas, if the job of a ship's captain was to keep it from sinking, he would never leave harbour. He suggests that while avoiding all risk may seem safe, it also limits what can be achieved. The goal for security, he argues, is to help organisations find a safe and defensible path to using AI effectively.
Why Trust Comes Before Speed
Meanwhile, Healy brings the conversation back to a more fundamental issue: trust. Databricks learned this the hard way inside its own operations. An early attempt at agentic threat triage used a single generalist model across multiple data sources and produced far too many false negatives. Swapping in smaller, specialist models for each source improved accuracy, a reminder that AI security maturity is built through iteration, not theory.
Healy breaks trust down into three main areas: auditability, so teams can understand how an agent reached a decision; limited data access, so agents only access what they need; and resilience against manipulation, particularly as agents combine multiple actions that could create greater risks.
That last point echoes something Khawaja raises later: the concept of contextual policies. Rather than static, role-based permissions that struggle to scale, contextual policies assess the actual risk of a sequence of actions in real time. Instead of users robotically approving every access request until they switch on "auto mode" out of fatigue, the system flags genuinely risky actions and lets routine ones pass - a smarter, more human-centred approach to access management.
From 97 Risks to a Focused Shortlist
The most useful takeaway from the episode is how Databricks approaches AI governance frameworks. Rather than starting with controls, the company's open, vendor-agnostic AI Security Framework, now in its third version, starts by mapping the AI system itself, then cataloguing the risks that could affect it. The list currently runs to 97 risks, nearly double what it was three years ago, largely due to the rise of agentic AI.
In practice, organisations are not expected to address all 97. Khawaja notes that a well-governed, modern data platform already neutralises a large chunk of them, leaving perhaps five to fifteen genuine concerns per use case. In this regard, each is mapped to specific, actionable controls rather than vague objectives. It's a philosophy borrowed from Khawaja's OODA loop framework (observe, orient, decide, act). This means most organisations are competent at observing problems and acting on them, but weak at the orientation and decision-making in between- the stage where risk triage happens.
Healy makes a clear case that a well-governed data layer is about more than compliance. It's a competitive advantage. Organisations with clean, contextual, well-governed data give their AI agents an edge that attackers who lack that internal context simply don't have.
The message from both guests is consistent throughout, and that is securing AI isn't about building the longest possible list of controls. It's about shrinking an intimidating problem down to something teams can actually solve, deliberately, iteratively, and without grinding the business to a halt. If you would like to find out more, please visit databricks.com or follow Omar Khawaja and Danny Healy on LinkedIn.
Takeaways
Challenges of deploying AI securely at enterprise scale.
Evolving security strategies for probabilistic AI systems.
Importance of AI governance and risk management.
Drawing a system picture for AI security.
Implementing controls and permissions for AI agents.
AI governance frameworks and standards.

Chapters
00:00 Introduction to AI security challenges in the enterprise
01:00 Misconceptions about securing AI and shadow AI risks
02:12 Learning from organisations that have secured AI
03:25 How AI changes cybersecurity strategies
04:52 The importance of understanding AI system architecture
06:50 The risks of banning AI versus managing it responsibly
08:37 Obstacles in operationalising AI securely
10:00 Building trust through model auditability and control
12:00 The role of external consultants and frameworks
13:03 Databricks' approach to AI security and governance
15:11 AI governance complexities and the UDA loop
17:27 Using risk-based controls instead of exhaustive controls
18:46 Designing permission controls for AI agents
21:52 Content and intent analysis for agent security
24:35 Developing effective AI security frameworks
27:47 Key takeaways for AI security and governance
29:28 Final thoughts on making AI security manageable