The Security Strategist

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 presenting an incident that way helps people understand what happened and retain the important details, he sees real value in it.
Can Analysts Trust AI Tools?
Trust depends on being able to follow how an answer was reached. That means traceability, sensible guardrails and a human being able to intervene when necessary. Tolbert points to one of the obvious problems with generative AI: a model can produce a detailed and convincing explanation that is simply wrong. Williams agrees, but points to another issue security teams will have to contend with. Investigators are accustomed to asking what filters were applied when reviewing a finding. With AI, the equivalent question becomes: what prompt produced this result? Even then, the same prompt may not return the same answer twice.
That makes a record of the process important. Teams need to be able to see what the system did, retrace the steps, repeat the analysis and share the results with others. Williams compares it to saving a query rather than relying on someone to remember exactly how they reached a finding. The need for control becomes more complicated when several visual views are connected. A network graph, timeline and map might all respond to the same request, but an AI system could apply different filters or handle the information differently in each view. A high-risk node might stand out in one representation and be overlooked in another. For Williams, that changes the question vendors need to answer. It is no longer only about how a visualisation looks, but about how reliably an analyst can control the system behind it.
His advice to CISOs is to try to draw what the AI-enabled security tool did during the previous week. If that is difficult, ask the vendor to show you. The same applies to value. If the system can demonstrate its impact visually rather than producing another lengthy report, that gives security teams something they can actually work with.
AI is likely to take on more detection and drafting, with some routine analysis running outside normal working hours. People will still need to step in when something is unusual, consequential or unclear. The useful systems will be those that can surface what matters without taking away the analyst's ability to investigate further when needed.
If you would like to learn more, listen to the full episode on The Security Strategist podcast, follow Dan Williams on LinkedIn, and find out more at cambridge-intelligence.com.
Takeaways
The evolution of graph visualisation in cybersecurity.
How AI enhances security investigations.
The role of visualisation in understanding complex data.
Challenges of trust and explainability in AI tools.
The shift from exploration to explanation in security analysis.
The impact of generative AI on incident narratives.
Balancing automation with human oversight in investigations.

Chapters
00:00 Introduction to cybersecurity visualisation and AI
02:48 Graph visualisation in cybersecurity and threat intelligence
05:37 The importance of visualisation in identity and fraud prevention
07:42 From exploration to explanation in AI-assisted security analysis
09:38 Combining AI with visualisation for better understanding
10:34 Generative AI and creating narratives for analysts
14:45 The importance of trust and explainability in AI tools
15:32 Grounding AI answers with visualisation and graph context
16:52 The need for traceability and control in AI applications
18:13 Data modelling guardrails for trustworthy AI in investigations
19:00 Multimodal visualisations for complex security data
20:19 Controlling AI visualisation outputs to ensure trust
21:43 The human analyst's role in AI-driven investigations
22:11 Balancing automation and human oversight in security analysis
23:29 Narrative storytelling and interactive visualisations
24:50 The evolution of user-friendly cybersecurity visualisation tools
25:26 Using visualisation to focus analyst attention
26:23 Hollywood's depiction of cybersecurity and its lessons
26:46 Advice for CISOs on integrating AI and visualisation
28:26 Final thoughts and key takeaways for security leaders

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.

Chapters
00:01 Business logic abuse and the 90% figure
05:34 Shadow, zombie and deprecated APIs
11:18 Telling abuse from legitimate use
22:20 AI agents and behavioural detection
42:35 Budget and ownership

Connect with Jonathan Care on LinkedIn
Connect with Uri Dorot on LinkedIn
Explore more episodes on The Security Strategist podcast
#APISecurity #Cybersecurity #Radware #TechPodcast #CloudSecurity #Infosec #ArtificialIntelligence #businesslogic #APIGateway #AITraffic #AIBotTraffic #GuardrailsforAI

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

Chapters
00:00 Introduction to Self-Improving AI Agents and Security Risks
02:30 What Are Self-Improving AI Agents and Their Current State
05:15 Challenges and Risks of Recursive Self-Improving AI
08:38 Real-World Use Cases and Deployment in Enterprises
11:08 Security and Governance Challenges of Autonomous AI
15:50 Case Study: Hugging Face Cyber Attack and AI Behavior
21:25 Future Outlook and Risks of Self-Improving AI in Business
22:12 Key Takeaways for C-Suite Leaders

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