The Security Strategist

Security operations centres have always been a numbers game with too many alerts, few analysts, and never enough hours in a shift. But something has changed. The arrival of AI models capable of identifying vulnerabilities and generating working exploits at machine speed has quietly shifted the terms of engagement between attackers and defenders. In this episode of the Security Strategist podcast, Richard Stiennon sits down with Edward Wu, founder and CEO of Dropzone AI, to unpack what the Mythos era actually means for the SOC and what defenders need to do about it right now.
The Alert Problem That AI Was Always Going to Solve
Wu didn't come to this conversation theoretically. Before founding Dropzone AI, he spent eight years at Palo Alto Networks building AI and machine learning detection products, systems that generated millions of security alerts. The conclusion he walked away with was that most security teams don't need another alert cannon. What they need is help processing the ones they already have.
That insight shaped everything about Dropzone's approach. The problem in most SOCs isn't a lack of signals, but it's analytical capacity. SIEMs stack-rank alerts by criticality, which sounds helpful until you realise that even a well-tuned system routinely surfaces 150 critical alerts per shift. No team handles that volume consistently. The alerts at the bottom of the queue, the ones that often contain the earliest indicators of a breach, simply never get looked at.
"AI can look at 50 alerts in parallel," Wu explains, and that's not a trivial capability. It means the lows, mediums, and informational alerts that security teams have historically deprioritised out of necessity can finally get attention. Several of Dropzone's customers have gone further; they've actually reversed years of detection tuning alerts that were switched off because they were deemed too noisy, because AI augmentation means the team now has the capacity to handle the volume. The aperture widens. Coverage improves, and holes in the detection fabric get closed rather than quietly accepted.
Mythos Changed the Timeline, Not the Outcome
When Anthropic published its findings on Mythos, the cybersecurity community took notice. Here was a model demonstrably capable of analysing code, discovering vulnerabilities, and writing working exploits with tasks that had previously required significant human expertise and time. Wu was watching closely, and his take is more measured than most of the commentary that followed.
He wasn't surprised. Models had been trending in this direction for some time, and when researchers revisited older models with better prompt engineering after the Mythos announcement, many found comparable outputs. What Mythos represented wasn't a sudden leap into unknown territory; it was confirmation that a step-function in attacker capability had arrived, and that the timeline for impact was no longer theoretical. "It was never a question of if," Wu says. "Mythos made the answer to when very concrete within the next couple of months."
The strategic implication is important to sit with. Vulnerability management is a slow-moving discipline with significant organisational friction. Patching schedules, competing priorities, and legacy infrastructure, these constraints don't bend quickly, regardless of how capable AI becomes on the offensive side. If attackers can now discover and weaponise vulnerabilities faster than defenders can patch them, the perimeter becomes harder to hold. Initial footholds become easier to gain.
This shifts the weight of the entire security programme toward detection and response. Wu frames it as a change in where the statistical advantage lies. Before a breach, attackers only need to be right once. But once they're inside, the math flips. On average, an attacker needs to make seven to ten moves to reach their objective. Detection and response teams have multiple opportunities to catch them, if the tripwires are sensitive enough, and if someone is actually paying attention to them.
Fighting AI with AI
The phrase "fighting AI with AI" risks sounding abstract. Wu brings it back to operational reality. The most immediate application is alert investigation, still the most labour-intensive function in any detection and response team. AI agents can begin processing an alert within seconds of it being created. Mean time to response drops. Mean time to disposition drops. The window of opportunity for an attacker to move laterally, escalate privileges, or exfiltrate data gets materially smaller.
For larger teams, this translates into improved coverage and faster response. For smaller teams, it functions as genuine force multiplication; analysts spend less time on repetitive investigation work and more time on detection engineering, threat hunting, and closing gaps in the broader security architecture.
Wu also addresses the hallucination concern that comes up whenever AI is proposed for high-stakes environments. His answer is direct: "Hallucinations are caused by poor context engineering." Feed a model insufficient or irrelevant information, and it fills in the gaps. Feed it the right data, the specific logs, the relevant threat intelligence, and the contextual detail it needs, and it performs the analytical task accurately. The model isn't the problem. The scaffolding around it is what determines the outcome.
For CISOs considering where to start, Wu's advice is practical. Audit where the team is actually spending its time. Identify the bottlenecks. Then evaluate vendors — at least three, in production, in your own environment, against three criteria: does the technology work now, is the company's roadmap aligned with where you're trying to get to, and can you trust the engineering team to deliver it?
The Mythos era hasn't changed the fundamental cat-and-mouse dynamic of cybersecurity. But it has raised the stakes and raised the ceiling on what AI-augmented defence can deliver. If you want to find out more, visit Dropzone AI or connect with Edward Wu on LinkedIn.
Takeaways
AI models like Mythos and their capabilities
Impact of AI on vulnerability discovery and exploit creation
Enhancing SOC efficiency with AI augmentation

Chapters
00:00 Introduction to AI in Cybersecurity
02:04 The Challenge of Alert Overload
07:00 The Impact of Mythos on Vulnerability Management
11:58 Detection and Response as the New Frontline
16:27 Fighting AI with AI: Practical Implications
22:00 Customer Experiences and Success Stories
25:15 Preparing for Automation in SOCs

Podcast: The Security Strategist
Guest: Nitay Milner, Co-Founder & CEO at ORION Security
Host: Richard Stiennon, Chief Research Analyst at IT Harvest, Author, and Advisor to Vendors, VCs, and Private Equity Firms
Cybersecurity is evolving every second, and Data Loss Prevention (DLP) has become a key focus for enterprises seeking to protect sensitive data. However, traditional DLP systems often struggle to keep pace with the scale of data in motion.
In this episode of The Security Strategist Podcast, host Richard Stiennon, Chief Research Analyst at IT Harvest, Author, and Advisor to Vendors, VCs, and Private Equity Firms, sits down with Nitay Milner, Co-Founder & CEO at ORION Security. They discuss how DLP has changed and the new dynamics of AI for data security and data security for AI.
They explore the challenges faced by traditional DLP systems, the need for deep contextual insights in data protection, and the implications of AI as both an enabler and a risk. The conversation highlights the shift from static, policy-based approaches to dynamic, AI-driven solutions, emphasising the importance of real-time monitoring and accurate, enforceable data exfiltration prevention.
What are the Limitations of Traditional DLP
Traditional DLP systems have existed for decades, but they mainly aim to protect stored data. These systems rely on fixed policies and rules that usually lack the context needed for smart security decisions. According to Milner, these systems cannot effectively manage data in motion, which is where data leakage typically occurs.
Traditional DLP notoriously generates high numbers of false positive alerts. Milner cites an alarming statistic stating that some enterprises employ as many as 60 DLP analysts just to triage these alerts, creating a bottleneck in security processes resulting in critical alerts slipping through the cracks due to unmanageable signal-to-noise ratios.
What are the Key Challenges in Real-World Applications
Milner shares his experiences at Cisco, where he worked with large enterprises like T-Mobile and Chevron. Even after putting traditional DLP measures in place, these enterprises continually struggled to protect their data effectively. Their challenges included the lack of real-time monitoring and an excessive focus on compliance instead of true data protection.
AI and agentic approaches to cybersecurity are helping enterprise data security teams today win the fight against data loss. Agentic DLP can analyse data in context, understanding both the data itself and the circumstances of its movement.
Milner notes that AI can interpret the source, destination, and nature of the data being handled. This allows AI systems to distinguish between legitimate business activities and potential data leaks. For example, if a financial analyst accesses sensitive information to complete a report, AI can identify this as a valid action rather than flagging it as suspicious.
How is AI Impacting DLP
A major benefit of adding AI to DLP systems is the decrease in false positives. Traditional methods often depend on deviations from set baselines, resulting in thousands of alerts lacking context. AI, particularly through Large Language Models (LLMs), can offer a better understanding, leading to smarter alerts and more efficient security responses.
As enterprises increasingly adopt AI technologies, it becomes essential to have strong DLP systems that can incorporate AI innovations. Security professionals need to focus not only on protecting data but also on enabling the safe use of AI within enterprises.
However, Milner spotlights the need to set guardrails around AI applications. As employees use AI tools for a variety of tasks, they can unintentionally expose sensitive information. By creating clear guidelines and monitoring systems, enterprises can keep data secure while still benefiting from AI.
Introducing AI into business processes brings new challenges, especially regarding data exploitation. Milner cautions that as AI systems become more common, the risk of sensitive data being shared with untrusted third-party applications rises. Enterprises must be careful about what data is shared and with whom to effectively reduce these risks.
Leveraging AI is not a question anymore; it’s how you do it that matters. Enterprises can create smarter, more efficient DLP systems that reduce noise, improve real-time data protection, and allow businesses to use AI safely. As we move into this new era of cybersecurity, the partnership between AI and DLP will be vital in protecting sensitive data.
Key Takeaways
Legacy DLP tools generate an overwhelming number of false positives.
AI can provide real-time contextual understanding.
Traditional DLP systems are not equipped for the scale or movement of modern data.
The future of data security relies on AI-native and agentic solutions.
Guardrails are essential for safe AI usage in enterprises.
Real-time monitoring is crucial for effective data protection.
Policies should be limited and focused on specific use cases.
AI can recognise risky data patterns that traditional methods cannot.
Data security must adapt to the rapid evolution and adoption of AI tools and agents.
Education on new risks is vital for enterprises.

Chapters
00:00 The Evolution of Data Loss Prevention (DLP)
02:54 AI's Role in Redefining Data Security
06:12 Challenges of Traditional DLP Systems
09:02 The Need for Contextual Understanding in DLP
12:07 Guardrails for AI in Data Security
15:04 Transitioning from Policies to AI-Driven Solutions
17:54 Real-World Examples of Data Protection
20:49 The Future of DLP and Data Security
For more enterprise AI in cybersecurity and DLP insights, please follow Orion Security across its official channels:
Website: ORION Security
YouTube: @ORION-dlp
LinkedIn: ORION Security

For more information on enterprise tech analyst-led insights, please visit em360tech.com
EM360Tech YouTube: @enterprisemanagement360
EM360Tech LinkedIn: @EM360Tech
EM360Tech X: @EM360Tech

Artificial intelligence has moved well beyond the chatbot era. The systems being deployed today don't just respond to questions; they plan, decide, and act. In this episode of the Security Strategist, host Trisha Pillay sits down with Kevin Curran, Professor of Cybersecurity at Ulster University and IEEE senior member, to unpack what this shift means for organisations, security teams, and the people responsible for keeping data safe. From prompt injection to privacy by design, this conversation covers the full spectrum of what agentic AI brings to the cybersecurity table and what it demands of us in return.
From Chatbots to Autonomous Agents
For years, AI in the enterprise context meant tools that waited for instructions. You asked, it answered. The dynamic was predictable, and security teams could build controls around it. Fast forward today, that world is rapidly becoming a memory.
Agentic AI represents a step-change. These systems don't sit idle waiting for a prompt; they pursue goals, interact with APIs, browse the web, execute code, and coordinate with other AI agents, often with minimal human involvement. As Curran explains, this autonomy is both the point and the problem. "Our surface area has dramatically expanded," he notes, capturing in a single phrase what security architects are grappling with across industries.
The implications are immediate. Traditional security frameworks were designed around human actors, meaning slow-moving, auditable, and accountable. Agentic systems operate at machine speed, across multiple endpoints simultaneously, and can chain together dozens of actions before a human reviewer even knows a task has begun. The perimeter, as security professionals understood it, has effectively dissolved.
For organisations still thinking about AI security in terms of data privacy policies and acceptable use clauses, this is a wake-up call. The threat model has changed. The question is no longer just what data an AI can access, but what actions it can take and on whose behalf.
The Vulnerabilities Nobody Warned You About
As the capabilities of agentic AI grow, so does the attack surface. Curran highlights prompt injection as one of the most pressing and underappreciated threats in this new landscape. Unlike traditional software vulnerabilities that exploit code, prompt injection attacks exploit the AI's core function: its ability to read and follow instructions.
The attack is deceptively simple. A malicious actor embeds hidden instructions in content that the AI will encounter, a webpage it browses, a document it processes, or an email it reads. The agent is unable to distinguish between legitimate directives and injected commands, following the hidden instruction. It might exfiltrate data, take an unauthorised action, or silently alter its behaviour. The user never knows.
This vulnerability is particularly dangerous in agentic contexts precisely because these systems have broader permissions and longer action chains. An AI agent with access to calendars, emails, file systems, and external APIs is a high-value target. A successfully injected prompt doesn't just compromise a single response but it can compromise an entire workflow.
The accountability question compounds the problem. As Curran puts it: "Who's responsible when AI acts autonomously?" When an AI agent makes a decision that causes harm, whether through a security breach, a compliance violation, or an erroneous action. The lines of responsibility blur in ways that existing legal and organisational frameworks aren't equipped to handle. Boards, legal teams, and CISOs need to be asking this question now, before an incident forces the issue.
The principle of least privilege emerges here as a critical mitigation. Curran is clear that AI agents should operate with the minimum access necessary for any given task, not a blanket set of enterprise-wide permissions. Limiting scope limits damage. If a compromised agent can only touch what it needs for a specific transaction, the blast radius of any attack is contained.
Secure by Design
The answer to agentic AI's security challenges isn't to slow down adoption, it's to build differently. Curran is a strong advocate for the secure by design philosophy, which holds that security must be an architectural decision made at the beginning of a system's life, not a layer of controls bolted on after deployment.
This principle has been discussed in cybersecurity circles for years, but agentic AI gives it new urgency. When you're deploying systems that make autonomous decisions, the cost of a security oversight isn't a patching cycle, it can be an incident. Designing for security from day one means conducting AI-specific threat modelling before a system goes live, mapping out what an agent can access, what actions it can take, and where the failure points lie.
Privacy by design sits alongside this as an equally vital framework. Curran points to ephemeral transaction models as a promising approach, structures in which AI agents handle sensitive data only for the duration of a specific task, with no persistent storage of information that isn't necessary. "Privacy by design minimises data collection," he explains, and in a world where autonomous systems are constantly processing personal and organisational data, minimisation isn't just good practice. It's good governance.
Tools and platforms are beginning to emerge that support this approach. Signing room technologies, for instance, offer ways to conduct sensitive transactions with built-in auditability and access controls, worth exploring for organisations managing AI-assisted workflows involving contracts or identity verification. Security scanning platforms designed for AI-era codebases are also maturing, giving development teams the ability to identify vulnerabilities before they reach production.
Organisations that treat security and privacy as foundational to AI deployment, rather than compliance requirements, will be better positioned as these systems become more capable and increasingly embedded in critical operations.
Takeaways
Agentic AI and autonomous decision-making
Security vulnerabilities in AI systems
Secure by design principles for AI deployment
Invest in AI-specific threat modeling
Implement security by design principles from the start
Adopt ephemeral transaction frameworks for privacy

Chapters
00:00 Introduction to Agentic AI and Cybersecurity
04:07 Understanding Agentic AI and Its Implications
09:50 The Shift from Assistive Tools to Autonomous Agents
15:46 Emerging Threats in AI Security
22:02 Secure by Design: Building Security into AI Systems
27:51 Privacy by Design in Autonomous Transactions
29:46 Conclusion and Future Outlook on AI Security

The moment an organisation's board starts asking how to prepare for autonomous AI attacks, the conversation has already shifted. What used to be a theoretical briefing topic is now a line item in risk registers and a direct question landing on CISOs' desks from the C-suite.
Shachar Hirshberg and Dan Shiebler, co-founders of Artemis Security, an AI-Native Protection Platform for security operations, in production at Mercury, Lemonade, Wix, Upwork, and some of the largest enterprises in the world, have that conversation daily.
Artemis raised $70M in series A, led by Felicis with First Round Capital and Brightmind Partners doubling down, alongside top VCs including Theory Ventures, Lockstep, Two Sigma Ventures, and prominent cybersecurity industry leaders, including the founders of Abnormal AI and Demisto, the former CEO and CTO of Splunk, and senior executives from CrowdStrike, Palo Alto Networks, Microsoft, and Okta.
In a recent episode of the Security Strategist Podcast with host Richard Stiennon, Hirshberg and Shiebler laid out the strategic reality with unusual clarity, not as a product pitch, but as a candid assessment of where the threat environment stands and what it demands from security leadership.
The Economics of Attack Have Changed
The foundation of legacy security architecture rests on an assumption that no longer holds: that launching a sophisticated, targeted attack is expensive. Acquiring intelligence on a specific organisation, crafting adaptive exploits, and manually steering a multi-stage breach required time, skill, and resources. Defenders could lean on that cost. Understand attacker behaviour, get ahead of their patterns, and you impose meaningful friction.
Shiebler identifies this as the core structural failure of traditional approaches today.
"AI really changes that. It's so much easier for attackers to craft new attacks, to explore different strategies, and make it much cheaper to send out radically different, really sophisticated attacks, which really means that trying to rely on approaches that involve just understanding attackers and trying to stay ahead of that is very, very challenging."
The consequence is not simply faster attacks. It's the collapse of the distinction between opportunistic, broad-based threats and sophisticated targeted campaigns. What previously required nation-state resources or advanced persistent threat infrastructure can now be approximated by an attacker with limited technical knowledge and access to capable agentic tooling.
The MTTR Calculation
Hirshberg frames the urgency in operational terms. The industry benchmark for mean time to respond sits at roughly four hours. The top 0.1 per cent of security operations globally measure in minutes. The frontier measures in seconds and adversaries are already in seconds.
"We are still talking in hours and need to bridge that gap because we will live in an era where it will have a hundred real zero days every single day in every organisation. If you're measuring your MTTR in hours and you have a hundred real attacks per day, you are fully overwhelmed with traditional tooling."
The arithmetic is unambiguous, and no staffing model resolves it. No incremental tooling investment closes it. It requires a categorical shift in how detection, investigation, and response are architected, moving from human-executed to human-guided autonomous response.
The Defender’s Unused Advantage
Underneath the operational urgency Hirshberg and Shiebler describe, sits an architectural premise about how Artemis is built. In an AI era, both sides draw on the same technology. Whatever edge the defender once held in raw capability is gone. What remains, and what the attacker cannot acquire from outside, is knowledge of the defender's own environment. Who works where. What is normal for this user? Which systems matter to the business? Whether a 3 a.m. login is routine or the first in this person's history. That knowledge has always existed. What has never existed is a security platform that could assemble it, keep it continuously current, and detect against it at machine speed.
Artemis is built around that advantage. The company calls it Environment Intelligence, and the practical effect for the security team is a qualitatively different output. Where most platforms produce alerts that an analyst then has to investigate, Artemis produces decision-grade cases: findings that arrive ready to act on.
The Strategic Cybersecurity Imperative

Hirshberg and Shiebler are blunt on timing, and it is the part that leaders miss. Deploying the technology is the fast part: Artemis connects in under an hour and produces real cases within 48 hours. The slow part is organisational: governance, and process maturity for a human-supervised AI to act at machine speed. That work compounds in months, not weeks. Organisations starting now will be operating in the new model when the threat tilts.
For more information on this, visit https://artemissecurity.com/ or connect with the guests:
Shachar Hirshberg | LinkedIn | Co-Founder and CEO Artemis
Dan Shiebler | | Linkedln | Co-Founder and CTO Artemis
Takeaways
AI transforming cyber operations
AI-driven attacks and defense
Limitations of traditional security architectures
How Artemis Is Shaping Autonomous Cyber Defence

Chapters
00:00 — The Evolving Cybersecurity Landscape
03:40 — AI in Cyber Operations
09:19 — Challenges of Traditional Security Architectures
14:03 — The Future of Cyber Defence
20:05 — Adapting to New Threats
25:29 — Strategic Planning for CISOs

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 Attack Surface Has Outgrown
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.
Why Traditional Security Approaches Fall Short
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.
The Shift AI Has Made Possible
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.
What Security Leaders Should Take Away
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
Takeaways
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

Chapters
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