The Security Strategist

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 considered from the start. Once sensitive information has been exposed through an AI workflow, fixing the problem afterwards may be too late. The shift towards AI means organisations need to rethink how they classify, protect and control data. Instead of relying only on network security, organisations need controls that stay with the data wherever it goes. Listen to the full episode and learn how Mattermost and Virtru are helping organisations take a data-first approach to security. Visit mattermost.com or virtru.com to learn more, and follow Dr Bill Anderson and JP Ayyappan on LinkedIn.
Takeaways
Rethinking zero trust by putting security closer to data.
The role of AI in enhancing data security and decision support.
Challenges of data classification and access control in AI environments.
The concept of data self-description and attribute-based access control.
Treating AI agents as quasi-human entities in security models.
The importance of disciplined data tagging and classification.
Risks of data spillage and the need for proactive safeguards.
Evolving from perimeter-based to a data-centric security model.

Chapters
00:00 Introduction to data security challenges in the AI era
01:59 Bill Anderson's background and experience in security and cryptography
03:53 The role of AI in security and decision-making in defence and intelligence
07:00 Breakdown of traditional security models and the shift to data-centric security
09:53 Zero trust principles and moving beyond network perimeter
12:58 The importance of human decision-making in security and AI integration
16:46 Data sharing, control, and the concept of self-describing data
22:00 AI's influence on data classification and the risks of data leakage
25:54 Treating AI agents as resources with their own identities
30:12 Best practices for organisations embracing AI and collaboration

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