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Security in the AI Era

A leadership guide to preparing for cyber risk in the age of AI — and why organizations need to act now.

By Kiran Sahota

The security landscape changed faster than most organizations expected. AI didn't gradually shift the threat environment — it collapsed the timeline. The tools attackers now have access to are the same tools defenders are still learning to use. That asymmetry is the problem.

This is a guide for executive and security leadership on what has changed, what it means, and what to do about it.

The new threat baseline

Three data points define where we are.

The new threat baseline

AI-enabled offensive capability is no longer theoretical.

1,000s

Zero-day vulnerabilities

detected by Anthropic's Mythos model across major technology platforms

73.1%

Exploit success rate

public Claude models detecting and exploiting real vulnerabilities

8 mo.

The capability gap

approximate lag between frontier global models and Chinese counterparts (Deepseek)

Sources: Anthropic Mythos model research (2026); Stanford HAI AI Index 2025

Finding and exploiting software vulnerabilities using AI models is real. Organizations should be prioritizing remediation in months, not years.

Anthropic's Mythos model detected thousands of zero-day vulnerabilities across major technology platforms. Public Claude models already operate at a 73.1% exploit success rate against real vulnerabilities. And the gap between frontier global models and Chinese counterparts like Deepseek is approximately eight months — meaning capability that looks exclusive today becomes widely available by end of year.

The conclusion is direct: finding and exploiting software vulnerabilities using AI is no longer a research problem. Organizations should be measuring remediation timelines in months, not years.

What has changed

The pre-AI security model assumed attackers were constrained by human speed, human cost, and human expertise. A sophisticated attack required skill, time, and resources. That constraint provided a natural rate-limiter on risk.

In the AI era, those constraints are gone. Reconnaissance, vulnerability discovery, and exploit generation can all be accelerated by orders of magnitude. The question is no longer whether your perimeter can resist a determined human attacker — it is whether it can withstand an autonomous AI agent running at machine speed, continuously, at no marginal cost.

The timeline

The window for action is narrow and closing.

The timeline

The window for action is short and getting shorter.

Now

FIX

Act as if bad actors already have the agents

Mythos-level capability is currently limited to select partners. Use this window to scan your assets thoroughly and remediate all known vulnerabilities. This lead time will not last.

6–12 months

DIFFUSE

Public models will close the gap

Frontier advances diffuse rapidly. Patch velocity becomes the new baseline expectation. Organizations without rapid remediation muscle will fall permanently behind.

12–24 months

PARITY

Autonomous agents at scale

Sophisticated attackers run AI agents continuously. Cyber risk will materially impact organizations that are not prepared. This is the trajectory of current capability curves.

The threat timeline: three phases from today’s narrow window to full AI-agent parity for sophisticated attackers.

The organizations that use the current window well will be in a materially different position when capability reaches full parity. The ones that treat this as a future problem will discover it is a present one.

Four imperatives for security leadership

Against this backdrop, CISOs and executive sponsors need to organize around four disciplines.

Hygiene — see everything you own and patch at machine speed. You cannot protect what you cannot see. Every internet-exposed asset needs to be discovered, inventoried, and continuously monitored. Patching cadences measured in quarters are no longer acceptable. Critical CVEs need to be addressed in days.

Leverage — use AI to defend before bad actors do. The same capabilities that power offensive tools power defensive ones. AI-augmented vulnerability triage, code review, and threat detection are not optional enhancements — they are the only way to match the velocity of AI-powered attacks. Organizations that delay AI adoption in their security function are unilaterally disarming.

Resilience — make it easy to contain. When something goes wrong — and in a high-velocity threat environment, something will — the speed of containment determines the scope of damage. Zero standing privileged access in production, strong segmentation, and tested incident response playbooks are the foundation of resilience.

Governance — treat AI itself as critical infrastructure. AI systems are now operational infrastructure. They need to be inventoried, approved, and monitored the same way any critical system is. An approved-model registry covering 100% of production AI use is not a compliance exercise — it is operational hygiene.

Five pillars with ownership and outcomes

Five pillars — with ownership and outcomes

Each pillar is actionable with clear ownership. Strategy without accountability is a slide deck.

01

Know what you own

CIO + CISO

100% of internet-exposed assets discovered within 24 hours

02

Patch at machine speed

CISO + Head of Engineering

Critical CVEs in production patched within 72 hours, 95% of the time

03

AI-augmented defense

CISO + Head of BC/DR

AI in ≥80% of triage and code-review workflows by Q4

04

Re-think your defenses

CISO + Head of Infrastructure

Zero standing privileged access in production

05

Govern AI itself

CISO + CAIO + General Counsel

Approved-model registry covers 100% of production AI use

Five pillars of AI-era security — from asset hygiene to AI governance — each with defined ownership and a measurable target outcome.

What to do this quarter

If you are a CISO or executive sponsor, the first move is an honest assessment of where your organization sits across these five pillars. Not where the roadmap says you will be — where you are today.

  1. Asset inventory. Can you enumerate every internet-exposed asset right now, within 24 hours? If not, that is the first gap to close.
  2. Patch velocity. What is your current mean time to patch a critical CVE in production? If it is measured in weeks, that number needs to come down before the threat environment catches up.
  3. AI in defense. Are you using AI tools in your security workflows today? Planning is not enough. Attackers are not planning — they are running.
  4. Privileged access. Do you have standing privileged access in production? Every account that exists is a potential attack vector.
  5. AI governance. Do you have a registry of all AI models running in production? If not, you are governing a system you cannot fully see.

The organizations that address these questions methodically over the next two quarters will be in a materially stronger position when the threat environment reaches full parity. The ones that treat it as a future problem will discover it is a present one.

Sources