The Security Strategist

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

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.
What is Ceros built for?
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.
Moving Beyond Passwordless to Agent-Bound Trust
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.
Why Casey Says the Time to Deploy Is “Immediately”
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.
Key Takeaways
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.

Chapters
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
What Cybersecurity Enterprise Strategies are Missing?
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
Is Drift the Quiet Failure AI is Accelerating?
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.
Where AI Offers Real Value
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.
Key Takeaways
Configuration drift is the hidden cause of modern cyber risk
AI is accelerating both cyberattacks and security failures
Security teams can’t keep up with expanding attack surfaces
Too many cybersecurity tools are underused or misconfigured
Prevention is making a comeback in cybersecurity strategy
AI-driven automation must be controlled, not fully autonomous

Chapters
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:

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.
Has AI Surpassed Experimentation Across Enterprises?
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?
Key Takeaways
AI agents behave like employees and need the same level of security oversight.
Most AI risk sits in the API layer where actions actually happen.
Faster AI systems can turn small security gaps into major threats.
Unmonitored “shadow AI” tools are quietly exposing sensitive data.
Continuous visibility is the foundation of securing any AI ecosystem.

Chapters
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
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