Show Notes
Many organisations assume that moving to the cloud means much of their security posture is handled automatically. But that assumption can create blind spots. In the latest episode of the Security Strategist Podcast, Trisha Pillay from EM360Tech speaks with Rob Edmondson, Senior Director of Product Marketing at CoreView, about cyber resilience in Microsoft 365 environments and what tenant hardening means in practice. As organisations rely more heavily on Microsoft 365 for collaboration, identity management, and device control, understanding how the environment is configured becomes increasingly important for security teams. Microsoft 365 Has Grown Beyond Its Original Scope When Microsoft first introduced Microsoft 365 as Office 365, it primarily focused on email and productivity tools. Security strategies often revolved around protecting inboxes and ensuring that business data was backed up. According to Edmondson, that model no longer reflects how the platform is used today. Microsoft 365 now includes a wide range of services that support identity management, device management, compliance, and collaboration. Many of these services sit at the centre of daily business operations. This shift means that security risks are no longer limited to email or file storage. Identity platforms, collaboration tools, and endpoint management capabilities all operate within the same tenant. If critical settings are misconfigured, the impact can extend across multiple systems at once. For security leaders, the challenge is recognising that the platform has evolved into something far more complex than many organisations initially planned for. Why Visibility Into Configurations Is Still Limited One of the main themes in the discussion is visibility. Edmondson explains that many organisations simply do not have a clear view of how configurations change within their Microsoft 365 tenants. Attackers often exploit these blind spots. If they gain access to an environment, they may modify configurations that allow them to regain access later. Because some of these changes are subtle, they may go unnoticed for long periods. However, not all configuration drift comes from attackers. Administrative errors or platform updates can also change settings in ways that affect security or operations. This is why documentation still plays a role. Edmondson suggests that even basic records of key configurations can help organisations understand their environment and recover faster during incidents. While documenting every setting in a large tenant may not always be practical, identifying and tracking the most critical configurations can provide a starting point for stronger oversight. Reducing Privilege and Strengthening Tenant Resilience Another concern discussed in the episode is the issue of excessive privileges. Many administrator roles in Microsoft 365 grant access across an entire tenant, which can increase risk if those accounts are compromised. Edmondson argues that reducing standing privileges should be a priority. Instead of granting broad permissions by default, organisations should consider limiting administrative access to only what is necessary. Tenant hardening plays an important role here. By tightening configuration controls and carefully managing privileges, organisations can reduce the likelihood that a single compromised account leads to a wider security incident. The goal is not simply to add more security controls, but to build a clearer understanding of how the tenant operates and how it could be restored if something goes wrong. The full conversation on the Security Strategist Podcast explores these challenges in greater depth, including configuration visibility, tenant recovery scenarios, and the practical steps security teams can take to improve resilience in Microsoft 365 environments. If you would like to find out more, visit coreview.com Chapters 00:00 Introduction to Cyber Resilience in Microsoft 365 01:01 Guest Introduction: Rob Edmiso
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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
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