The Security Strategist

You’re in an argument with your AI bot on ChatGPT; suddenly, your screen is locked. None of the keys on your keyboard work, and then you see a ransom note displayed on the screen. The systems have been encrypted, and the incident response team has been activated. The executives go to the one thing they had been told would save them, which is the backups.
Enterprises may believe their data is safe because of their immutable backups. But according to Mark Grazman, CEO of Fenix24, they are likely mistaken and often realise this after ransomware has already hit them.
At some stage of a ransomware attack, the assumptions of cybersecurity come up against reality.
In the recent episode of The Security Strategist podcast, host Richard Stiennon, Chief Research Analyst at IT-Harvest, is joined by Mark Grazman, CEO and Co-Founder of the ransomware recovery company Fenix24. They discuss the critical aspects of ransomware resiliency, including the four pillars of recoverability—survivability, completeness, speed, and assurance. They also talk about how enterprises can better prepare for and respond to attacks.
When Stiennon asked Grazman what's the thing he would assess that incident response playbooks miss if he walked into an active incident right now. Grazman says after a scoping call, he would ask the affected enterprise if their data was immutable. Most people say yes.
“There’s an 84 per cent chance that they’re wrong,” he adds. “The attack already happened, the data's already gone, and they don't even know it yet.”
The issue, he says that enterprises are practising and simulating that the data’s gone along with the infrastructure. “They're practising that there was a hurricane or a fire or a replication or an event as opposed to a true ransomware.”
Also Read: Ransomware Attacks: What You Need to Know
Find the latest cybersecurity insights, podcast episodes, and expert analysis on EM360Tech.cpm. Visit fenix24.com for more information.
Takeaways
84% of enterprises may be wrong about backup immutability.
Surviving backups do not guarantee successful recovery.
Ransomware recovery depends on four pillars: survivability, completeness, speed, and assurance.
Critical applications rely on more infrastructure than enterprises often realise.
Traditional disaster recovery tests may not reflect a ransomware attack.
Cybersecurity budgets need more investment in recovery readiness.

Chapters
00:00 Introduction to ransomware resiliency and Mark Grazman's expertise
01:20 Assessing incident response priorities in real-time attacks
02:06 The myth of immutable data and common misconceptions
03:06 Breaking down the four pillars of resiliency
04:03 Survivability: Protecting critical data and dependencies
05:02 Completeness: Ensuring full data and infrastructure recovery
07:32 Speed: Rehydration, containment, and infrastructure considerations
09:30 The importance of assurance and continuous testing
11:09 Applying resiliency principles to other disasters
12:37 The gap between enterprise expectations and reality
13:05 Evolving offence and defence in ransomware protection
14:39 Pre-attack preparedness and the Argos platform
16:06 The process of resiliency assessment and tuning
19:18 Organisational roles and collaboration for effective recovery
21:18 Key message for CISOs, CIOs, and CEOs on resiliency
23:30 Closing remarks and resources for further information

Security teams around the world have always tried to play a balancing act when it comes to authentication. If there are too many measures put in place, people will always find a way to get through it. In the world of rapid AI advancement, this balancing act is proving to be more difficult for organisations. The reason is that AI agents proliferate; they're now able to perform tasks on behalf of employees and customers without a human overseeing every action. So what needs to be done to prevent your organisation from being exposed?
On this episode of the Security Strategist Podcast, host Trisha Pillay talks with Dan Moore, Senior Director of CIAM Strategy and Identity Standards at FusionAuth, about how and why identity has become the new security perimeter. Moore has worked for almost six years at FusionAuth, starting in developer relations before stints in sales engineering and implementation prior to his current role. At FusionAuth, he helps track standards bodies like the IETF and OpenID Foundation and determines which fledgling methods are ready for adoption into the product.
The Security-Usability Tension Gets Sharper
Finding the right balance between strong security and a smooth user experience is a challenge organisations have faced for years. Moore traces it back to the invention of the first password field in the 1960s. Various industries have adopted different approaches to ensure that there is a balancing act of strong security and a smooth user experience for their customers. For example, banks are willing to require more security checks than a consumer app because the risks are so much higher.
The old methods of authentication were designed for a world where every login belonged to a person making decisions at human speed. This has all changed now because of AI agents. Unlike people, AI agents can work independently, run continuously, and complete thousands of tasks in seconds. This speed and scale mean they can also cause far more damage in a matter of seconds if something goes wrong. AI agents need to work independently, so traditional human-focused security measures like MFA and CAPTCHAs often get in the way. It's also important to know that removing those checks doesn't just eliminate the security risks. This simply means those risks can happen so much faster. At the same time, asking humans to approve everything isn't a solution either, because people quickly become overwhelmed and stop paying attention.
Adaptive Authentication in Practice
This is where identity is shifting from a single check at the door towards continuous and contextual verification. Moore describes it as moving away from a binary model, because risk no longer lives only at the login screen. It follows the session, the device and the ongoing behaviour within an application. FusionAuth worked with a platform connecting caregivers with families needing support, a sector handling sensitive data including that of minors. By adding enterprise single sign-on and multi-factor authentication, the company cut its authentication development time by 90 per cent and opened up business markets it previously couldn't serve, proof, Moore says, that tighter security and a better user experience aren't mutually exclusive when the approach is intelligent about context.
Giving AI Agents Their Own Identity
One of the biggest shifts discussed is the need to stop thinking of AI agents as just another user account. Instead, organisations need to manage them as separate digital identities with their own permissions and controls. Moore recounts a colleague mentioning they would let an AI assistant drive their browser while logged in as themselves. This becomes indistinguishable, from the system's perspective, from the person acting directly. Without a separate identity, there's no way to apply different policy, add extra checks, or restrict what an agent can do relative to its human counterpart. With all that said, it's no wonder that AI agents need their own identities, provisioning, and scope, along with their own audit trail. The risk comes down to velocity. A compromised employee can only do so much before they're detected, but a misbehaving AI agent can make thousands of decisions, access systems, and execute actions in the same amount of time.
Moore frames trust as resting on three interlocking layers: identity validation, audit, and policy enforcement. Validation establishes who or what is acting; audit records what actually happened, which matters given how unpredictable agent behaviour can be; and policy enforcement, built on principles like least privilege and short-lived, task-scoped credentials, limits the damage if something goes wrong. All three layers work together to build trust, he says, because each one compensates for what the others struggle to catch alone.
His advice for organisations still finding their footing is to start small rather than wait for a polished strategy: inventory the AI agents and automated processes already running, note what kind of credentials they rely on, and begin shifting static API keys towards short-lived, standardised grants. Above all, he argues, AI identities deserve their own category tied to an accountable human or team, but never simply reused from existing human or service accounts. If you would like to find out more about this, visit FusionAuth or connect with Moore on LinkedIn.
Takeaways
The changing role of identity in security.
Challenges of AI-powered applications and autonomous agents.
Adaptive authentication and risk-based security.
Building trust through identity validation, audit, and policy enforcement.
Practical steps for organisations to enhance security in AI environments.

Chapters
00:00 Introduction
01:28 Guest background and role at Fusion Auth
03:07 The security-usability tension in identity management
04:13 Impact of AI and autonomous agents on security
05:56 Balancing security controls with user experience
09:02 The shift to adaptive, context-aware authentication
11:48 Real-world example of security and usability balance
14:04 AI identities versus human identities
17:53 Building trust in AI systems with layered security
23:34 Practical steps for organisations to prepare for AI security
27:30 Closing remarks and resources

Prioritisation is the way to tackle enterprise data challenges. It may seem like a simple solution, and it might be too. If you’re an enterprise overwhelmed by vulnerabilities in data, especially with the evolution of AI and automation, this conversation is for you.
In the recent episode of The Security Strategist podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, sat down with James Walta, Vice President of Product Management at Brinqa. The agenda for this episode was to break down why enterprises are overwhelmed by vulnerability data. Additionally, Walta lays out a strategic plan of action to help enterprises prioritise vulnerabilities proactively rather than reactively.
The discussion builds on the previous episode, where Brinqa CSO Brad Hibbert and host Richard Stiennon, Chief Research Analyst at IT-Harvest, talked about how AI is helping attackers with faster scanning, smarter exploit chaining, and machine-speed intrusions.
Walta continues this conversation with EM360Tech’s Dua, focusing on prioritisation in exposure management strategies. He puts up a case noting AI will not rescue security teams from unorganisation unless the underlying data is ‘good’ and reliable.
Takeaways
Context is crucial for effective cybersecurity management.
The chaos in cybersecurity is amplified by AI-driven vulnerabilities.
Data quality is foundational for prioritisation and remediation.
Patching faster is not always the best approach; understanding risk is key.
Operational clarity can be achieved by unifying asset visibility.
Prioritisation must be based on business context and asset sensitivity.
AI can help but may also amplify confusion if data is poor.
CISOs should focus on outcome metrics rather than activity metrics.
Effective vulnerability management requires a clear understanding of ownership.
The conversation around cybersecurity must evolve to address real risk reduction.

Chapters
00:00 Navigating Cybersecurity Chaos
02:52 The Importance of Context in Cybersecurity
06:07 Bridging the Gap: From Vulnerability Detection to Remediation
09:09 Understanding Risk Over Speed
11:46 Enhancing Data Quality for Better Decision Making
14:57 Operational Clarity: Transforming Overload into Insight
18:05 Measuring Success Beyond Vulnerability Counts

Visit brinqa.com for more information on how enterprises should prioritise vulnerabilities proactively.
Vulnerability Management, Exposure Management, Cybersecurity Strategy, AI in Security, Risk Prioritisation, Brinqa, EM360Tech, The Security Strategist, Cyber Risk, Data Quality, CISO, Threat Exposure Management, Asset Visibility, IT Security, Risk Reduction, James Walta

Every CISO out there faces one key challenge: the challenge of getting the board to acknowledge cybersecurity as a top enterprise risk management priority.
According to the ClearPoint Strategy Strategic Planning Report, only 51 per cent of active strategic and corporate projects maintain a steady Green status. The remaining 49 per cent fluctuate between Amber and Red, requiring varying levels of intervention.
This goes to show that many investment decisions are reliant on red, amber and green dashboards and not on financial exposure. According to Mike Saxton, CRO at MyCiso, the issue relates to cyber reporting often lacking portability. “A director may be highly experienced and commercially sophisticated, but still struggle to compare risk posture between organisations because the underlying reporting models are inconsistent.”
With AI also in the picture now, the speed and scale of attacks is rapidly rising; that gap is becoming harder to defend.
This is why in the recent episode of The Security Strategist podcast, E360Tech’s host Shubhangi Dua, Tech Journalist and Podcast Producer, was joined by Asdrúbal Pichardo, CEO at Squalify, 3x SaaS CEO, Board Advisor, Start-Up Mentor, Non-Executive Director.
This podcast breaks down how to actually turn technical risk into something the rest of the business can realistically manage, measure, and report on.
Translating Cyber Risk for the Boardroom: A CISO’s Guide to Financial Quantification
Pichardo says when it comes to cyber risk, it's time to avoid reporting based on qualitative metrics; instead, portray more quantitative metrics. This means really talking to the executives and the boards in the language of business “which is money.”
"CISOs need to rely less on qualitative assessments. They need to translate the cyber risk into financial figures so the board will understand the implications of cyber."
CISOs typically present cyber risk through technical metrics, maturity scores and vulnerability reports, but boardrooms tend to avoid making decisions based on technical language. This is why translating that cybersecurity technical jargon into metrics is essential for boardrooms. They think in terms of financial exposure, business resilience and return on investment (ROI).
The CEO of Squalify explains why the future of cybersecurity leadership depends less on explaining threats and more on quantifying business impact. He puts up a case for enterprises requiring a common language that is comprehensible by both security teams and executives instead of relying on technical dashboard data.
Leveraging AI Vulnerability Detection: The Strategic Advantage of Mythos
Artificial intelligence (AI) has made it more complex from every corner. AI-driven cyber attacks are on the rise. On the other side, AI is being deployed by defenders to protect their platforms as well as to optimise the speed and effectiveness of AI tools and integrate it into their workflows. Ultimately, AI has, for better or worse, blurred the line between cybersecurity, governance and business continuity.
To put into perspective, Dua asked Pichardo about Anthropic's Mythos model’s incredible vulnerabilities-spotting capabilities. He said that Mythos is causing a lot of dialogue in the industry right now, but the vulnerability-discovering capabilities had existed for years, and those tools went unnoticed.
While industry individuals may be concerned about attackers taking advantage of AI tools like Mythos, enterprises should be able to access the same technology to identify and fix those weaknesses before attackers exploit them. However, geopolitical tensions and other economic disparities have made it hard for enterprises to access.
The key idea is that defenders have an advantage because they know their own systems. He says, “The hacker doesn't have the knowledge, or the source code from your enterprise. You already have it, so enterprises need to get there with Mythos before the hacker comes to you with Mythos.”
The issue he spotlights is that American companies have been given access to Mythos, but the US government has restricted access outside of the nation.
“At the end it should it should it should get into the right hands because it's probably already in the wrong hands,” the CEO states.
The conversation around Anthropic’s Mythos model depicts a shift in the enterprise tech and cybersecurity industry. While much of the discussion has focused on how attackers might exploit increasingly capable AI, Pichardo sees the greater opportunity for defenders.
Also Read: Fraud Tops CEO Cyber Concerns as Ransomware Attacks Continue to Surge
Converting Cyber Risk into Strategic Investment
For boardrooms, the new question they must pose is whether enterprises are optimising AI quickly, efficiently, and, most of all, safely to minimise risks before adversaries get to it. The recent cyberattack by a rogue OpenAI AI model on Hugging Face was an eye-opener for all. In a worst-case scenario, imagine if the hackers’ AI agents began penetrating secure enterprise tech platforms at a rate that’s hard to fend off their strikes.
AI has moved from being a technical capability to a strategic investment decision one that should be measured in business impact rather than technology adoption.
“If you can demonstrate that the likelihood of experiencing a disruption because of AI is higher in numbers, that will change the minds of any boardroom. This applies to any industry, from public sector and banking, financial, manufacturing, defence, energy,” notes Pichardo.
He added that at Squalify’s mother company, Munich Re, the world's largest cyber reinsurer ensures that AI’s impact on cyber risk is visible not only from a technical perspective but a business perspective as well.
"It goes beyond tech or IT; it's processes, governance, business operations.”
Ultimately, enterprises need to quantify cyber risk so they are better able to defend against the AI-driven threat landscape at any given time.
Takeaways
Cyber risk quantification is becoming a boardroom necessity
AI is increasing attack velocity, not just sophistication
Defensive AI can create a competitive advantage
Cyber and AI risk are converging into enterprise risk
Board AI literacy is becoming a strategic capability

Chapters
00:00 Understanding Cyber Risk in Business
02:42 The Differences in Cyber Risk Management: US vs Europe
05:42 The Evolution of Risk Management with AI
08:46 Quantifying Cyber Risk: The Role of Squalify
11:34 Real-World Applications: Onboarding Clients at Squalify
14:53 The Importance of Financial Metrics in Cybersecurity
17:38 AI's Impact on Cybersecurity and Business Operations
20:20 The Future of AI in Cyber Risk Management
23:32 Key Takeaways for CISOs and Board Members

Watch the full episode of The Security Strategist Podcast to hear Asdrúbal Pichardo discuss cyber risk quantification, AI governance, boardroom communication and what enterprise leaders should prioritise next.

The one key thing that could aid enterprise success in the agentic AI cybersecurity space today is its ability to understand agents' intent. It’s easy to state but hard to convert into an actionable security strategy.
This is why in the recent episode of The Security Strategist podcast, host John Tolbert is joined by Dror Zelber, VP Product Marketing at Radware and Dhanesh Ramachandran, Product Marketing Manager at Radware. They got together to discuss the emerging challenges of agentic AI security and how to tackle it realistically.
More specifically, they break down what it actually means to secure AI agents, whether they are interacting with internet-facing applications on behalf of users or operating within enterprise environments, emphasising the importance of behavioural monitoring and visibility in managing AI agent interactions.
The conversation further spotlighted the need for enterprises to establish trust and governance frameworks for AI agents while prioritising security budgets and strategies.
Also Watch: Unmasking the Invisible Threat: Defend Your APIs Before Attackers Do
Takeaways
Agentic AI introduces new security challenges compared to traditional applications.
The attack surface for AI agents is significantly broader and easier to exploit.
Behavioural monitoring is crucial for understanding AI agent actions and intent.
Enterprises must prioritise visibility into AI agent activity.
Trust and identity verification are key challenges in the agentic era.
CISOs should allocate budgets for AI security solutions early in the deployment process.
Strict policies and governance are necessary for deploying AI agents.
Monitoring and auditing are essential to prevent unauthorised actions by agents.
Enterprises need to distinguish between beneficial and risky AI agent behaviour.
The future of security lies in enabling beneficial AI interactions while maintaining safeguards.

Chapters
00:00 Introduction to Agentic AI Security
01:25 Emerging Security Challenges with Agentic AI
05:37 Traditional Security Measures vs. Agentic AI
10:44 Current Activity of AI Agents in Enterprises
14:29 Distinguishing Beneficial vs. Risky AI Agent Activity
17:13 Establishing Agent Identity and Authority
23:18 Priorities for CISOs in the Agentic Era

Watch the full episode for complete insights on autonomous AI agents, the future of AI cybersecurity and how enterprises can effectively manage risky AI agent behaviour while still taking advantage of the agentic technology. For further information, visit radware.com.