The Security Strategist

Most organisations believe they have a solid grip on their security posture. They invest in tools, run penetration tests, and build out security teams. Yet when a breach happens, the entry point is often an asset no one was monitoring, something unknown, unmanaged, and fully exposed.

That gap between perceived security and actual exposure is the core challenge Rob Gurzeev has spent his career trying to solve. In this episode of Security Strategist, host Richard Stiennon speaks with Rob Gurzeev, CEO of CyCognito, to unpack the realities of external attack surface management and why many organisations continue to fall behind despite years of investment.

The scale of the problem is difficult to overstate. Where an enterprise once managed a handful of websites and internal systems, it now contends with hundreds of thousands of applications, cloud assets, APIs, and connected devices, many of which were provisioned quickly, handed off between teams, or simply forgotten.

Gurzeev points out that in large enterprises, the number of externally exposed assets can reach into the tens of millions. Up to 50 per cent of those assets are often entirely unknown to the security team. They are not in any inventory. Nobody is patching or monitoring them. From an attacker's perspective, they are the most attractive place to start. This is the nature of the modern external attack surface, not a defined perimeter, but a constantly shifting sprawl of exposure that grows faster than most teams can track it.

The instinct for many organisations is to run more penetration tests. It is a reasonable response, but it addresses only a fraction of the actual risk. Manual pen testing, by its nature, is scoped and time-limited. Gurzeev is direct on this point: in environments with hundreds of thousands of assets, traditional testing leaves the vast majority of the attack surface unexamined. The result is a false sense of security; teams believe they have assessed their exposure when, in practice, they have assessed a small and carefully selected slice of it. The big issue is visibility. Security investments have historically been built around known assets, things that are already in the inventory, already behind a firewall, already being monitored. The unknown assets fall outside that perimeter entirely, and it is precisely those assets that attackers seek out.

This is where the conversation turns. AI has fundamentally changed what is achievable in attack surface management, and Gurzeev is clear about the practical impact: real-time threat detection, at scale, across the entire external surface, not just the assets that are already known. Continuous automated testing now makes it possible to assess every exposed asset, not a curated sample of them. Vulnerabilities that would previously have gone undetected for months can now be surfaced within hours. The economics have shifted as well. The prohibitive cost of testing at scale, which once made comprehensive coverage impractical, has been dramatically reduced. For CISOs and CIOs operating under resource constraints, that matters. The question is no longer if comprehensive coverage is possible. It is whether the organisation has decided to pursue it.

Visibility is not something organisations can assume; it has to be actively built and continuously maintained. In large enterprises, unknown assets often make up the bulk of real exposure, rather than being a marginal risk. AI-driven tools are now making it possible to assess this landscape continuously and at scale. In this context, mean time to remediation becomes the defining metric separating organisations that actively manage risk from those that only measure it. Thinking like an attacker means asking a simple question: which of our assets does nobody know about? The answer to that question is where the real work begins. For more on external attack surface management and enterprise cybersecurity, visit cycognito.com. Connect with the guest:

Rob Gurzeev: LinkedIn | Co-Founder & CEO, CyCognito

• External attack surface complexity
• Impact of AI on cybersecurity
• Strategies for attack surface visibility
• Continuous monitoring is essential, not one-off assessments
• Proactive exposure management reduces breach risk

00:00 – Introduction to External Attack Surface Challenges

01:02 – Rob Gurzeev's Background and Focus on Attack Surface Management

02:42 – From Intelligence to Cybersecurity: Rob's Journey

04:51 – Why Organisations Lack Clear External Attack Surface Visibility

07:43 – The Growing Complexity of IT Environments

11:27 – Vulnerability Management vs Attack Surface Management

13:20 – Challenges in External Attack Surface Discovery

17:05 – The Role of AI in Cybersecurity and Attack Surface Management

20:16 – Key Takeaways for CISOs and CIOs

Podcast: The Security Strategist

Guest: Jasson Casey, CEO & Co-Founder, Beyond Identity

Analyst: Richard Stiennon, Chief Research Analyst at IT-Harvest

In an enterprise technology market that’s saturated with AI copilots and coding agents, most enterprise security strategies are already outdated.

On the recent episode of The Security Strategist podcast, analyst Richard Stiennon, Co-Founder and Chief Research Analyst at IT-Harvests, presses Jasson Casey, CEO & Co-Founder, Ceros by Beyond Identity, on a question few vendors are answering clearly.

“How do you actually control autonomous agents once they’re inside your environment?” posed Stiennon.

Casey’s answer is architectural, focusing on Ceros – a new control plane from Beyond Identity built specifically for agentic workflows.

The problem Ceros addresses is practically faced by enterprises. For instance, enterprises deploying tools like Claude, Codex, or Copilot for coding and workflow automation are effectively granting agents the same privileges as human operators, but without equivalent oversight. These agents write code, call APIs, and interact with sensitive systems, often across long-lived sessions where risk can evolve in real time.

Casey points out that most enterprises fall into one of two active camps: those moving fast and accepting the risk, and those slowed by governance concerns. What both groups lack is visibility. Not logs after the fact, but live, session-level awareness of what agents are doing, what tools they’re invoking, and how their behaviour changes over time.

Ceros is designed to sit directly in that gap. Rather than acting as a perimeter control or identity gateway, it operates in tandem with agent sessions, exposing granular telemetry on tool calls, device posture, and execution context. The emphasis is not on blocking upfront, but on establishing a real-time inventory of agent activity—a prerequisite for any meaningful governance model.

Beyond Identity built its reputation on eliminating passwords, but Casey makes it clear that passwordless authentication was only the first step. The deeper issue is the portability of credentials themselves. Whether it’s a password, API key, or session token, anything that can be copied can be abused—and in agentic systems, that risk multiplies.

Ceros extends the company’s device-bound identity model into AI workflows. Instead of relying on bearer tokens, which Casey likens to “Willy Wonka golden tickets,” Ceros enforces cryptographic, device-bound sessions where every API request is uniquely signed. This approach draws on emerging standards like DPoP but applies them in a way that doesn’t require upstream API providers to change their architecture.

The result is a subtle but important shift. Security is no longer tied to possession of a token, but to the integrity of the device and session generating each request. For agents, this means their actions are continuously attributable, and any attempt to export or replay credentials simply fails. In practical terms, it collapses the blast radius of an incident to a single device and makes lateral movement significantly harder.

Perhaps the most striking moment in the discussion comes when Stiennon asks when organisations should introduce controls like Ceros into their agent pipelines. Casey’s answer is blunt: immediately. Not after pilots, not post-deployment hardening, but at the same time, agents are introduced.

That urgency reflects a broader shift in how enterprise risk is accumulating. AI agents are active participants in systems, capable of chaining actions, interacting with multiple tools, and amplifying both productivity and exposure. Retrofitting security after these patterns are established is, in Casey’s view, a losing strategy.

Ceros has been intentionally designed to avoid the friction that typically delays security adoption. Developers running AI-based workflows see no change in their experience, while security teams gain visibility and policy controls through the same interface. The initial deployment phase focuses on observation rather than enforcement, allowing enterprises to understand their agent footprint before introducing restrictions.

Ultimately, identity security must evolve from authenticating users to governing actions—human or otherwise—in real time. With Ceros, Beyond Identity believes that the future of enterprise security will be defined not by who logs in, but by what autonomous systems are allowed to do once they’re already inside. Teams can get their AI governance started on ceros.sh.

• AI agents are introducing major identity and visibility gaps across enterprise systems.
• Traditional “authenticate then trust” models fail in dynamic, long-running agent sessions.
• AI agents have no real identity. Ceros binds every agent action cryptographically to hardware, making credential theft pointless and every action attributable to a specific user and device.
• Ceros gives security teams identity, visibility, and control over AI agents — enforcing policies at the proxy layer before agents can act, not after. Get started at ceros.sh.

• 00:00 Emerging Security Gaps in AI Coding Agents
• 03:03 The Role of Governance in AI Deployment
• 05:58 Beyond Identity: The Passwordless Revolution
• 09:00 Device-Bound Credentials and API Security
• 11:59 Integrating Security Solutions for AI Agents

To learn more about Ceros and how agentic workflows in cybersecurity enterprises are changing, follow:

Beyond Identity LinkedIn: @Beyond Identity

Beyond Identity X: @beyondidentity

Beyond Identity YouTube: @BeyondIdentity

EM360Tech YouTube: @enterprisemanagement360

EM360Tech LinkedIn: @EM360Tech

EM360Tech X: @EM360Tech

Follow: @EM360Tech on YouTube, LinkedIn and X

Stay connected for more expert insights, podcast episodes, and enterprise data strategy discussions.

Podcast: The Security Strategist

Guest: Garrett Hamilton, CEO, Reach Security, and Jay Wilson, CIO & CISO, Insurity

Host: Shubhangi Dua, Podcast Producer and B2B Tech Journalist, EM360Tech

There’s a growing disconnect at the core of enterprise cybersecurity, and most enterprise leadership teams don’t recognise it yet. With budgets increasing, tools improving more than ever, and AI quickly being integrated into both offensive and defensive strategies.

On paper, this should be a golden era for cyber resilience. However, many enterprises feel more exposed, not less. The issue isn’t a lack of innovation, rather it’s something harder to see—and far more dangerous.

In this episode of The Security Strategist podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, sits down with Garrett Hamilton, CEO of Reach Security, and Reach customer, Jay Wilson, CIO & CISO at Insurity.

They unpack why enterprises are still getting breached despite record security spend—and how configuration drift, AI-driven threats, and operational blind spots are quietly reshaping the future of cyber defence.

They address the key issues enterprises are playing with in the industry today – whether what enterprises configured yesterday is still protecting them now. The reality is that it isn't safeguarding them.

“The surface area of the problem is just continuing to increase,” says Wilson. “But security teams aren’t growing at the same rate.” This mismatch is creating a new kind of exposure—one that doesn’t show up in dashboards.

Also Read: Ten Hidden Cybersecurity Misconfigurations

For years, cybersecurity strategies have focused on accumulation – collecting tools, more telemetry, and more layers of defence. For instance, respondents, on average, were dealing with 35 tools at a time. But as environments grow, they become harder to manage. The issue pertains to control, not to the visibility of risk.

“You had one product expert acting as five or six experts in one,” Hamilton explains. “That approach never scaled well.”

Today, this issue is worse. Teams inherit complex tools they can’t fully optimise or continuously validate. Over time, small changes—like exceptions, updates, and integrations—start to add up. No single change breaks the system, but together, they alter it.

Also Read: Configuration Lifecycle Management (CLM) That Reduces Complexity And Risk

This shift is what insiders are increasingly referring to as configuration drift. It’s becoming one of the most overlooked risks in cybersecurity. It’s not dramatic or invisible, but it’s constant.

“If it isn’t broken, don’t touch it—that used to work,” Isurity CISO says. “Not so much anymore.”

In a pre-AI world, misconfigurations could linger for months before being exploited. Now, that time frame has shrunk. “The adversary can find it faster than that three-month or six-month window,” Hamilton warns.

The new reality is that enterprises are no longer just defending against external threats. They are now racing to keep up with changes within their own environments. AI too is making the problem worse. For example, rapid “vibe coding” can quickly create solutions, but those solutions tend to fail without ongoing maintenance.

“It worked for two or three months,” the Reach CEO notes, alluding to customer experience pertinent to vibe coding. “Then I returned to it—and it wasn’t working as expected.”

Drift isn’t a bug but a byproduct of speed.

For the past decade, cybersecurity investments have focused heavily on detection and response. However, that model is starting to show its weaknesses. There are too many alerts, too much noise, and too many problems that shouldn’t be there in the first place.

“If you don’t emphasise the preventive side, you end up with a lot of unnecessary focus on detection and response,” Hamilton tells Dua.

The current shift is subtle but significant, with leaders now asking not just how quickly they can respond, but how many of those incidents could have been completely avoided.

This is where configuration integrity comes into play. It’s also where AI may finally offer real value—not as a substitute for analysts, but as a tool to continuously monitor, validate, and adjust security measures in real time.

Still, both Hamilton and Wilson are wary of too much automation. “I would not use automated remediation in my production environment,” Wilson states. “What if it broke something?”

The future shouldn’t be about fully autonomous security. Instead, it should focus on awareness, controlled automation—and that’s a much more complicated challenge to tackle.

There’s a tendency in cybersecurity to chase the next big thing—AI, zero trust, platform consolidation. But this discussion points to a more fundamental issue. The biggest risk might not be what’s new but what’s actually changing quietly.

“This is the most exciting time in 16 or 17 years of being in security,” Hamilton expresses. “But it’s also moving faster than we’ve ever seen.” For CISOs and CEOs alike, speed alters the dynamics.

Building the right architecture is a part of the goal, but now cybersecurity leaders should ensure the strategies are aligned consistently at scale. This is where most enterprises are falling behind.

1. Configuration drift is the hidden cause of modern cyber risk
2. AI is accelerating both cyberattacks and security failures
3. Security teams can’t keep up with expanding attack surfaces
4. Too many cybersecurity tools are underused or misconfigured
5. Prevention is making a comeback in cybersecurity strategy
6. AI-driven automation must be controlled, not fully autonomous

• 00:00 Introduction to Cybersecurity Challenges
• 02:52 The Role of AI in Cybersecurity
• 05:54 Configuration Drift: The Overlooked Risk
• 11:47 The Impact of Configuration Drift on Security
• 17:49 The Need for Visibility in Security Infrastructure
• 23:57 Balancing Detection and Prevention
• 29:49 The Future of AI and Automated Remediation

To hear how leaders are tackling configuration drift, AI-driven threats, and the growing control gap, listen to the full conversation with Reach Security on EM360Tech.com.

Find Reach Security’s Configuration Drift Report here. For more information, visit reach.security.

Reach Security LinkedIn: Reach Security

Reach Security X: @ReachSecurity

Reach Security YouTube: @ReachSecurity

EM360Tech YouTube: @enterprisemanagement360

EM360Tech LinkedIn: @EM360Tech

EM360Tech X: @EM360Tech

Enterprise AI, AI Security, Cybersecurity, API Security, Autonomous Agents, Agentic AI, Shadow AI, AI Governance, Enterprise Technology, Digital Transformation, Security Leadership, AI Risk, Data Protection, AI Compliance, Cyber Risk, CISO Strategy, AI Infrastructure, Emerging Technology, Enterprise Security, Salt Security

#AISecurity #EnterpriseAI #Cybersecurity #APISecurity #AgenticAI #AutonomousAI #ShadowAI #AIGovernance #EnterpriseSecurity #ArtificialIntelligence #AICompliance #DataSecurity #CyberRisk #TechPodcast #CISO #SecurityLeadership #GenerativeAI #AIInfrastructure #DigitalTransformation #CyberDefense #AIThreats #EnterpriseTech #SaltSecurity #EM360Tech #AIInnovation

Podcast: The Security Strategist podcast

Guest: Eric Schwake, Director of Cybersecurity Strategy, Salt Security

Host: Shubhangi Dua, Podcast Producer and B2B Tech Journalist

Adopting enterprise AI is often seen as a productivity boost. However, a subtler change is happening behind the scenes, and security leaders are still trying to understand it. Enterprises now not only optimise AI tools but are also bringing autonomous agents into their workplaces.

“We would call AI agents an additional workforce that enterprises are deploying,” says Eric Schwake, Director of Cybersecurity Strategy at Salt Security.

The description is more literal than it seems. These agents can access systems, interact with data, and perform multi-step tasks with little human input. Unlike employees, they lack intuition and caution.

In the recent episode of The Security Strategist podcast, Schwake sat down with Shubhangi Dua, Podcast Producer and B2B Tech Journalist to discuss AI agents, shadow AI, and API security challenges are transforming enterprise cybersecurity. Schwake explains how to secure autonomous AI systems at scale today.

AI is no longer in the experimental stage. Leadership teams across industries are actively promoting its use to boost innovation. Executives like Jensen Huang, Founder, President & CEO of NVIDIA, are highlighting a larger trend where enterprises are measuring, incentivising, and expecting AI adoption.

This urgency creates a familiar tension. Speed provides a competitive edge, but it also shortens the time available for governance. “You want them to use this innovation to do their work,” Schwake tells Dua. “But you don't want sensitive data leaking and getting into the wrong hands.”

Also Watch: What Happens to API Security When AI Agents Go Autonomous?

1. AI agents behave like employees and need the same level of security oversight.
2. Most AI risk sits in the API layer where actions actually happen.
3. Faster AI systems can turn small security gaps into major threats.
4. Unmonitored “shadow AI” tools are quietly exposing sensitive data.
5. Continuous visibility is the foundation of securing any AI ecosystem.

• 00:00 Introduction to AI and Cybersecurity
• 02:43 Insights from RSA Conference
• 06:30 The Role of AI Agents in Security
• 08:30 Transitioning from Discovery to Governance
• 12:03 Protecting Sensitive Data in AI Systems
• 15:21 Identifying Weak Points in AI Security
• 18:54 The Need for Measured Security Approaches
• 20:38 CISO Strategies for API Security
• 23:22 The Future of AI in Cybersecurity
• 25:14 Visibility as a Key Security Measure

For more information, please visit em360tech.com and salt.security.

To learn more about Salt Security and AI and API security, follow:

Salt Security LinkedIn: Salt Security

Salt Security X: @SaltSecurity

Salt Security YouTube: @SaltSecurity

EM360Tech YouTube: @enterprisemanagement360

EM360Tech LinkedIn: @EM360Tech

EM360Tech X: @EM360Tech

Enterprise AI, AI Security, Cybersecurity, API Security, Autonomous Agents, Agentic AI, Shadow AI, AI Governance, Enterprise Technology, Digital Transformation, Security Leadership, AI Risk, Data Protection, AI Compliance, Cyber Risk, CISO Strategy, AI Infrastructure, Emerging Technology, Enterprise Security, Salt Security

#AISecurity #EnterpriseAI #Cybersecurity #APISecurity #AgenticAI #AutonomousAI #ShadowAI #AIGovernance #EnterpriseSecurity #ArtificialIntelligence #AICompliance #DataSecurity #CyberRisk #TechPodcast #CISO #SecurityLeadership #GenerativeAI #AIInfrastructure #DigitalTransformation #CyberDefense #AIThreats #EnterpriseTech #SaltSecurity #EM360Tech #AIInnovation

Policy is the backbone of every effective cybersecurity framework. It defines how an organisation protects its data, governs access to critical resources, and dictates the rules that every firewall, endpoint, and identity system must enforce. Yet for most organisations, policy management is the one discipline they consistently get wrong.

In this episode of The Security Strategist, Chief Research Analyst Richard Stiennon sits down with Jody Brazil, CEO of FireMon, and John Kindervag, Chief Evangelist at Illumio and the father of Zero Trust, to dissect why cybersecurity policies fail, where the rot begins, and what it genuinely takes to build a security posture that holds.

Every discussion of cybersecurity eventually circles back to one uncomfortable truth, which is that technical controls are only as good as the policies that drive them. Firewalls, intrusion detection systems, and endpoint agents all execute instructions someone wrote down. If those instructions are incorrect, outdated, or in conflict, the tools become liabilities rather than defences.

Stiennon opened the conversation by framing this in concrete terms, as most organisations have accumulated years, sometimes decades, of firewall rules written by engineers who have long since left. Nobody knows what the rules do. Nobody wants to remove them in case something breaks. So the attack surface quietly grows, rule by rule, misconfiguration by misconfiguration.

• Policy rules accumulate over the years, with no regular auditing or ownership.
• Engineers who wrote original rules leave, taking institutional knowledge with them.
• Implicit trust zones create blind spots between internal network segments.
• Manual management of distributed devices introduces critical human error.
• Organisations lack unified visibility across multi-vendor firewall estates.
• Compliance-driven policy creation prioritises documentation over real protection.

Brazil's journey into policy management began not in a boardroom but at a terminal in the late 1990s, watching a misconfigured firewall bring a major financial institution to its knees. A single incorrectly written rule, one that should have been straightforward, caused a cascading failure that resulted in significant financial losses and reputational damage that took years to repair. The Firemon CEO said:

"It was that moment that it hit me. We need a solution to better manage the policies that are enforced on these devices. And that was the genesis of FireMon."

Kindervag's origin story is equally revealing, and it directly challenges a comfortable myth. Zero Trust is often described as a bold new philosophy, a paradigm shift invented in the halls of Forrester Research around 2010. Kindervag's account is more earthbound as the framework emerged from watching bad policy fail, over and over, in environments that assumed internal network traffic was inherently safe. The Illumio Chief Evangelist shared his thoughts:

"It said that you didn't have to have a policy statement or rule when you went from a high-trust zone to a low-trust zone. I thought that was silly — and I started putting out firewall rules on all interfaces. All of these systems should have the same trust level. And it should be zero. That's where Zero Trust comes from. It comes from bad policy."

Brazil and Kindervag converge on a shared conclusion that tools exist to solve this problem. The barriers are organisational inertia, institutional fear of breaking existing connectivity, and a lack of executive mandate to treat policy governance as a first-class security discipline.

FireMon's platform approaches the problem from the management layer, giving security teams unified visibility across multi-vendor firewall estates, automated rule analysis, change workflow management, and compliance reporting. Illumio's micro-segmentation platform approaches it from the enforcement layer, applying granular policy controls workload-to-workload, whether on-premises or in the cloud, without requiring network reconfiguration.

Together, they represent a maturity arc that Stiennon describes as increasingly urgent. As organisations migrate workloads to cloud environments, adopt containerisation, and expand their attack surface through remote work and third-party integrations, the traditional approach to policy management has been reactive, manual, and siloed by device, which is simply incompatible with operational reality.

Want to learn more about cybersecurity strategies? Visit firemon.com

• The evolution of cybersecurity policy and its impact on security architecture.
• The origins and importance of policy management in firewalls.
• Challenges of managing complex policies in large enterprises.
• The concept of zero trust and its relation to policy flaws.
• The role of micro-segmentation and graph databases in modern security.

00:00 The Foundation of Cybersecurity Policy

03:21 The Evolution of Network Security

10:10 Challenges of Firewall Policies

14:28 The Complexity of Network Segmentation

19:12 Understanding the Security Graph

23:24 AI and Vulnerability Management

29:45 Conclusion and Key Takeaways

In the digital age, securing sensitive business information has never been more critical. Microsoft 365 has become the backbone of operations for organisations worldwide, and with that centrality comes an expanding attack surface that many security teams are only beginning to fully understand. 

In a recent episode of the Security Strategist podcast, host Richard Stiennon sat down with Rob Edmondson, Senior Director of Product Marketing at CoreView, to unpack the practical realities of Microsoft 365 security. The conversation covered configuration drift, excessive privilege, tenant hardening, and the emerging security challenges posed by AI agents offering actionable guidance for security professionals at every level.

Microsoft 365 has changed significantly from a simple productivity platform into a comprehensive security concern in its own right. As Edmondson points out, the transition from Office 365 to Microsoft 365 marked a pivotal shift in how organisations utilise these tools. What began as a suite of familiar applications, such as Word, Excel, and Outlook, has grown into an interconnected ecosystem of over 60 apps and services, from Teams and SharePoint to Power Automate, Defender, and Purview. That expansion has delivered enormous productivity gains, but it has also multiplied the potential vectors for security vulnerabilities exponentially. Every additional service is a new configuration surface, a new set of permissions to govern, and a new integration that must be secured. Understanding this evolution is the essential starting point for any organisation serious about Microsoft 365 security.

Configuration drift is one of the most pervasive and underappreciated threats in Microsoft 365 environments. It refers to the gradual, often unnoticed divergence of system configurations from their original, secure baseline, which is a slow accumulation of small changes that individually seem harmless but collectively create significant vulnerabilities.

Edmondson highlighted that most organisations lack adequate visibility into how their Microsoft 365 tenant is actually configured at any given moment. Many still rely on manual methods like spreadsheets, periodic snapshots, and ad hoc reviews to track configuration state. This approach is fundamentally inadequate in environments where settings can change daily, sometimes through automated processes or third-party integrations that bypass normal change management controls.

The consequences of undetected configuration drift can be severe. Breaches have been traced directly to unauthorised or unintended configuration changes, a permissions setting quietly altered, an authentication policy weakened, or a data loss prevention rule inadvertently disabled. 

Excessive privilege is consistently ranked among the leading contributors to security incidents in cloud environments, and Microsoft 365 is no exception. When users, service accounts, and applications hold more permissions than their role requires, the potential blast radius of any compromise — whether through phishing, credential theft, or insider threat — expands dramatically. Edmondson walked through the practical challenge: in large organisations, permissions accumulate over time. A user gets temporary admin access to complete a project, and that access is never revoked.

As organisations adopt AI-driven tools and agents within their Microsoft 365 environments, a new and largely uncharted security frontier is emerging. AI agents — automated systems capable of acting on behalf of users, reading emails, accessing files, and executing workflows — introduce permissions challenges that most security frameworks were not designed to handle.

Edmondson was candid about the challenge: many organisations deploying AI agents do not have clear visibility into what those agents can access, what data they are interacting with, or whether the permissions they hold are appropriate. In an environment where an AI agent might have access to the entire Microsoft 365 data estate of a user or a team, the consequences of a misconfigured or compromised agent are significant.

The same principles that govern human access with least privilege, continuous monitoring, and regular review must be extended to AI agents. This requires both the technical capability to enumerate agent permissions and the governance processes to enforce appropriate boundaries. Organisations that deploy AI capabilities without first establishing this control layer are trading short-term productivity gains for long-term security debt.

In the fast-moving threat landscape, understanding and proactively strengthening your Microsoft 365 security posture is no longer optional; it is a business imperative. Configuration drift, excessive privilege, and AI agent governance are not edge cases; they are mainstream risks affecting organisations of every size and sector. The insights shared by Edmondson on the Security Strategist podcast provide a practical foundation for addressing each of these challenges with clarity and urgency.

By implementing continuous monitoring, enforcing least-privilege access, hardening your tenant configuration, and extending security governance to AI agents, organisations can significantly reduce their exposure and build a Microsoft 365 environment that is resilient by design. For further insights and tools to support your Microsoft 365 security journey, visit CoreView.

• Configuration drift and its impact on security.
• Excessive privileges and how to mitigate them.
• Tenant hardening best practices.
• Managing AI agents and permissions in Microsoft 365.
• Strategies for continuous security monitoring.

00:00 Introduction to Microsoft 365 Security

02:25 The Shift to Security Priority in Microsoft 365

04:30 Understanding Configuration Drift

09:09 Excessive Privilege and Its Risks

12:48 AI Agents and Identity Security

16:20 Tenant Hardening and Common Misconfigurations

18:36 Recommendations for Strengthening Security Posture

Financial crime is no longer a peripheral concern for banks and fintechs; it is a defining operational challenge. The pressure to grow transaction volumes, onboard customers quickly, and keep pace with increasingly sophisticated fraud actors has placed finance and compliance teams at the very heart of business strategy. For many institutions, the question is no longer how to use artificial intelligence in their fraud detection stack, but how to use it responsibly.

In this Security Strategist podcast, hosted by Jonathan Care, Senior Lead Analyst at KuppingerCole, he speaks with Kunal Datta, Chief Product Officer at Unit21, about the changes in financial crime prevention technology and the gaps that remain in the industry.

For most of the past two decades, financial crime prevention operated on one of two tracks. Larger, data-rich institutions invested in machine learning models capable of identifying complex behavioural patterns across millions of transactions. Smaller players, or those entering new product categories with thin data histories, tended to rely on rules-based systems, which are explicit, human-authored logic that flags transactions meeting predefined criteria.

Both approaches have genuine strengths. Rules-based systems are auditable, easy to explain to a regulator, and quick to update when a new fraud typology emerges. Machine learning systems are far more powerful at surfacing non-obvious correlations and adapting to evolving attack patterns, but they require substantial training data and significant engineering effort to deploy.

The arrival of large language models and generative AI has introduced a third paradigm, one that is fundamentally non-deterministic. Unlike a rule that fires predictably on every run, or an ML model that produces a consistent probability score for a given feature vector, a generative AI system may reason differently across identical inputs. This has profound implications for how institutions build, test, and govern their fraud detection infrastructure.

Perhaps the most underappreciated tension in financial crime prevention is not technical; it is commercial. Every fraud control is also a friction point. A transaction declined as suspicious is, from the customer's perspective, simply a transaction that failed. Every false positive erodes trust, damages conversion rates, and risks losing a customer to a competitor with a more permissive onboarding flow. According to Datta:

“Machine learning excels at identifying complex patterns, but rules-based systems can quickly adapt to new types of fraud that humans can spot with minimal examples.”

This means that fraud teams are never simply optimising for fraud prevention in isolation. They are solving a constrained optimisation problem that is minimising fraud losses while simultaneously protecting revenue, preserving customer experience, and staying within the bounds of what regulators require. AI can shift that frontier, enabling more precise risk assessment that reduces both fraud and false positives simultaneously. But only if it is deployed and governed carefully.

Looking forward, Datta sees the trajectory of AI in financial crime prevention pointing towards systems that combine the pattern-recognition power of machine learning with increasingly robust mechanisms for transparency and accountability. The goal is not to choose between a powerful AI and an explainable one — it is to build infrastructure that delivers both.

Several technical approaches are emerging to close this gap. Structured output formatting — requiring AI systems to return decisions in machine-readable formats like JSON, with explicit reasoning chains, makes it possible to audit AI behaviour at scale. Evaluation sets, which establish a curated baseline of labelled cases against which model performance is continuously benchmarked, allow institutions to detect drift and maintain defensible performance records. 

The institutions that will lead this space are those treating AI governance not as a compliance overhead but as a competitive advantage. A well-governed AI system is faster to get regulatory approval, faster to deploy new capabilities, and more resilient when regulatory scrutiny increases.

The most striking thread in Datta's thinking is his insistence on placing financial crime prevention within a broader moral frame. Financial crime is not merely an operational risk; it is a conduit for some of the most serious harms in the world: human trafficking, modern slavery, terrorist financing, and the systematic exploitation of vulnerable people. Viewed through this lens, the deployment of better AI in financial crime prevention is not primarily a business efficiency story. It is a contribution to a more just and safer world. Datta says:

“AI should be viewed not only as an efficiency driver but as a tool to address broader societal issues like human trafficking and exploitation. Better detection is a moral obligation.”

This framing matters for how organisations think about investment in financial crime technology. If AI in fraud prevention is purely a cost centre, it will always lose budget battles to revenue-generating activities. 

If you would like to find out more, visit: Unit21.ai or read more about Rules vs. Machine Learning: Finding the Best of Both Worlds by Kunal Datta.

If you are looking to strengthen how your organisation identifies and manages risk, you can request a personalised demo with Unit21.

• Evolution of financial crime detection over the last decade
• Deterministic vs non-deterministic AI systems in fraud prevention
• The role of generative AI and context engineering in compliance
• Accountability and explainability in AI-driven decision making
• Regulatory perspectives on AI and risk management

00:00 Navigating Financial Crime Prevention Challenges

02:54 The Evolution of Fraud Detection Systems

05:55 The Debate: Explainability vs. Performance in AI

08:51 Balancing Accuracy and Regulatory Expectations

12:01 Context Engineering in AI for Financial Crime

15:04 Rethinking Accountability in AI Systems

17:55 AI as a Societal Imperative in Risk and Compliance

Podcast: The Security Strategist

Guest: Rick Wagner, Senior Director, Product Management at SailPoint

Analyst: Jonathan Care, Lead Analyst, KuppingerCole

The identity security market is crowded, but a significant change is occurring below the surface. In a recent episode of The Security Strategist podcast, host Jonathan Care, Lead Analyst at KuppingerCole, sat down with Rick Wagner, Sr. Director Product Management at SailPoint.

In this episode, Wagner pointed out a growing gap between how enterprises manage access and how modern systems operate. As AI and machine identities grow rapidly, traditional models no longer work.

For years, enterprises have depended on periodic access certifications to manage access. However, such a model is proving to be weak. “Periodic access reviews only look at appropriate access at a point in time,” says Wagner, noting that “certification fatigue results in rubber stamping.”

The challenge is both scale and accuracy. With machine identities often outnumbering humans, governance processes designed for manual oversight are quickly becoming outdated. “Doing those certifications at agent speed is literally impossible,” he adds, emphasising the need for change.

Also Watch: Why AI Agents Demand a New Approach to Identity Security

The way ahead is real-time authorisation, which continuously checks if access is appropriate at the moment it is requested. “It’s not only appropriate— is it appropriate right now?” Wagner explains.

This change depends on context, incorporating information such as device health, user behaviour, and risk level. Frameworks like the Shared Signals Framework help enterprises implement this by allowing real-time data sharing across the security ecosystem. This approach leads to more dynamic, policy-driven access that keeps pace with AI systems.

At the same time, CISOs face the rise of shadow AI, an expanding network of agents operating with little oversight. “You can’t manage what you can’t see or what you don’t know about,” says Wagner, highlighting visibility as the first line of defence.

The long-term goal is autonomous identity governance, where systems continuously evaluate and adjust access based on risk. “As risk levels start to increase, we might add additional factors up to quarantining that access,” he explains.

In this new framework, identity becomes the core of cybersecurity strategy. As Wagner puts it, the ongoing challenge is urgent – determining “who has access to what—and is that access appropriate right now.”

• Real-time identity governance replacing static access reviews
• AI and machine identities outpace human oversight
• “Certification fatigue” is weakening traditional access controls, increasing risk through unchecked approvals.
• Non-human identities (AI agents, bots) are now the fastest-growing and least visible attack surface.
• Context-aware access decisions—based on risk, behaviour, and environment—are becoming the new standard.
• Visibility into agents and their permissions is critical: “you can’t manage what you can’t see.”
• Autonomous, risk-adaptive identity security is emerging as the end-state for modern enterprise cybersecurity.

00:00 Introduction to Identity Security in AI Era

06:54 Managing Privileged Access Risks

13:52 Real-Time Governance and Joiners, Movers, Leavers

20:14 Strategic Moves for CISOs in Agent-Based Operations

For more information, please visit em360tech.com and sailpoint.com.

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Over 95 per cent of leaders now say identity security is core to their strategy. A decade ago, this wasn’t even part of the conversation. The awareness is there, but awareness alone isn’t enough. Many organisations feel secure, yet the metrics they track often tell a different story. 

In this episode of Security Strategist, EM360Tech’s Trisha Pillay sits down with Craig Ramsay, Senior Field Strategist, and Rod Simmons, VP of Product Strategy at Omada, to unpack the State of Identity Governance 2026 report. Together, they explore why confidence in identity security doesn’t always equal true protection and how AI, non-human identities, and fragmented systems are changing the rules.

Many organisations focus on operational metrics that are easy to measure: provisioning speed, audit readiness, and compliance. These give a sense of efficiency but not necessarily security. Simmons explains: “We can provision identities faster, but that doesn’t tell us about inherent risks. Orphaned accounts, dormant privileges, unmanaged access—these risks often go unseen.”

Ramsay adds, “It’s like home security. You might feel confident, but when was the last time you checked your back door?”

The survey revealed a clear disconnect: strategic awareness exists, but organisations are not always measuring the right things. Security leaders should not only track completed tasks, but they must also understand where risk accumulates and how quickly they can respond to incidents. Risk-based metrics, rather than activity-based metrics, are the key to true governance.

Almost every organisation reports adopting Zero Trust principles. The execution often falls short. Policies may exist in pockets, but full implementation requires connected systems that can share signals in real time. Without this integration, Zero Trust becomes a concept rather than a functioning model.

Rod highlights the issue: “It’s one thing to want continuous evaluation, but another to have systems that actually support it. Shared signal frameworks are essential for consistent enforcement across the enterprise.” Until Zero Trust principles are fully integrated across all platforms, access control and identity governance will remain reactive rather than proactive.

Identity is no longer just about people. Non-human identities, but API keys, service accounts, and AI agents, are multiplying at unprecedented rates. Some organisations see 150 non-human identities for every human. These identities act autonomously, persistently, and at scale. Simmons explains the challenge: “With human identities, we ask what access they have. With non-human identities, we ask what they can do, and what they’ve done.”

Ramsay adds a crucial reminder: “Artificial intelligence still needs an accountable individual. Human oversight is essential, even as AI agents scale and operate independently.”

These agents create both risk and opportunity. They can automate governance, improve provisioning, and flag anomalies—but without proper visibility and ownership, they become a blind spot. Over 40 per cent of surveyed organisations admitted their AI agents still use static credentials, a simple but serious vulnerability.

One thing is for sure: you cannot govern what you cannot see. Visibility is the foundation. Only once organisations know what exists, who owns it, and how it behaves can they secure identities, human and non-human alike, effectively.

Identity security is no longer a back-office concern—it’s strategic. Organisations must move from confidence to proof, from operational reporting to risk measurement, and from fragmented controls to integrated governance. AI and non-human identities are not just a challenge; they are an opportunity to rethink how identity security can truly enable business, not just protect it.

For more insights on effective identity governance strategies, check out Omada's State of Identity Governance 2026 Report.

• Over 95 per cent of security leaders now see identity as a core strategy. Identity isn’t optional anymore.
• Feeling secure doesn’t equal being secure. Many organisations track efficiency, not actual risk.
• Non-human identities are multiplying fast. 
• Zero Trust adoption is growing, but integration gaps remain. 
• AI in identity governance works, but always keep a human in the loop.

00:00 Introduction to Identity Governance and Security Challenges

02:55 Insights from the State of Identity Governance Report

05:53 The Gap in Security Confidence and Measurement

08:53 Operational Metrics vs. Risk Indicators

11:50 Zero Trust Adoption and Implementation Challenges

14:54 The Role of AI in Identity Governance

17:52 Non-Human Identities and Governance Challenges

21:07 Key Takeaways for Security Leaders

Podcast series: The Security Strategist

Guest: Amit Megiddo, CEO and Co-Founder, Native

Host: Richard Stiennon, Chief Analyst Researcher at IT-Harvest

In the recent episode of The Security Strategist Podcast, Amit Megiddo, CEO and Co-Founder, Native, joins host Richard Stiennon, Chief Research Analyst at IT-Harvest, to discuss a growing challenge in enterprise cloud security. Enterprises are investing heavily in cloud providers’ built-in controls, yet risk persists when those controls are not consistently enforced across complex environments.

According to Megiddo, the problem isn't a lack of tools, but a failure to make them work effectively. Drawing on his experience launching Amazon GuardDuty at Amazon Web Services, the Native CEO explains that enterprises have hit a tipping point. The challenge is no longer about visibility. It is about executing at scale across complex multi-cloud environments.

Cloud providers such as Amazon Web Services, Microsoft Azure, Google Cloud, and Oracle Cloud offer a wide range of built-in security features. Yet, as Megiddo points out, most enterprises are only using a small part of what is available.

“The easy part is turning controls on,” he says. “The hard part is making sure they consistently deliver security results.” This is where many enterprises struggle. Security teams create policies, but platform teams carry them out. In the process, vital context is lost. The result is a disjointed approach where risks are identified but not effectively managed.

Megiddo calls this the “execution gap.” It is a fundamental issue in how enterprises handle cloud security. Even with sophisticated CSPM and CNAP tools, organisations remain mostly reactive. They are relying on detection and fixing problems instead of preventing them.

The podcast spotlights a key shift in enterprise security strategy – moving from detection controls to proactive, policy-driven enforcement. Conventional methods focus on spotting issues—like unencrypted or publicly exposed data—and then starting remediation processes. However, as cloud environments grow, this method becomes untenable.

Megiddo suggests embedding security directly into the architecture:

• Preventing non-compliant resources from being created
• Designating approved regions for workloads
• Enforcing network isolation rules for sensitive environments, such as AI training workloads

This “secure-by-design” approach turns security from a reactive task into a core operational control. However, implementing this is not easy. Enterprises must translate high-level policy goals into thousands of low-level settings across various cloud providers, each with its own APIs, services, and policy frameworks.

“It’s not just about writing the policy,” Megiddo emphasises. “It’s about safely rolling it out, simulating impact, managing exceptions, and ensuring it stays enforced over time.”

It creates new operational needs such as simulation tools, drift detection, real-time developer feedback, and automated exception handling. Essentially, cloud security becomes a continuous process rather than a one-time setup.

The main takeaway for enterprise leaders is that cloud security is no longer just about managing risks; it is becoming an edge in the market. As major providers continue to invest heavily in native security features, the real differentiator will be the ability to coordinate and enforce those tools effectively.

Megiddo’s vision is straightforward: a unified control system that lets enterprises define security intent once and apply it consistently across cloud and hybrid environments.

In an industry shaped by AI, multi-cloud complexity, and rapid digital changes, this ability could determine how quickly—and securely—enterprises can progress. For CISOs and IT leaders, the message is clear: the future of cloud security lies not in observing more, but in doing more—with precision, consistency, and scale.

• Shift from detection to proactive, policy-driven cloud security to reduce risk.
• Multi-cloud across Amazon Web Services, Microsoft Azure, and Google Cloud requires unified enforcement.
• CISOs need tools that turn security policy into automated controls.
• Secure-by-design cloud architecture protects AI and enterprise workloads.
• Strong cloud security execution drives scalability and resilience.

• 00:00 The Cloud Security Landscape
• 03:11 Challenges in Implementing Cloud Security
• 08:00 Transitioning to Proactive Security
• 12:26 The Evolving Role of Security Leaders
• 16:42 Future Trends in Cloud Security

For more information, please visit em360tech.com and native.security.

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Native LinkedIn: https://www.linkedin.com/company/native-security/

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