Frontier AI models could be an adversary's force multiplier

Combating frontier AI model risks and threats

In this evolving landscape, organisations must address the risks and threats posed by frontier AI models by combining human expertise with AI-assisted defenses and treating security as a continuously adaptive function rather than a periodic exercise. CISOs need to establish new policies, operational procedures, and governance models not only to defend against the misuse of frontier AI but also to strategically leverage these technologies to strengthen the organization’s overall security posture. Let us explore how CISOs can adapt to manage and mitigate the emerging risks associated with frontier AI models.

Continuous exposure management

CISOs need to shift from traditional monitoring to continuous exposure management. In the age of AI, quarterly assessments are too slow when AI can continuously analyse attack surfaces. Security teams should prioritise continuous asset discovery, external attack surface management, automated configuration validation, and rapid patch orchestration tailored to AI entities. Equally important is reducing the “blast radius” of inevitable compromise through zero-trust segmentation, least-privilege access, short-lived credentials, and robust identity governance. The assumption should be: if AI can find it, it will eventually be exploited.

AI-aware defence engineering

This mechanism reflects the integration of AI-focused threat modeling into the SDLC and infrastructure design. Development pipelines should include AI-assisted code review, secret scanning, dependency risk analysis, and automated policy validation before deployment. Focus on securing high-risk AI infrastructure components, such as APIs, plugins, agents, MCP-style integrations, and AI-connected workflows, which significantly expand the attack surface. Defenders need behavioral analytics to detect abnormal automation patterns, autonomous reconnaissance behavior, and machine-speed lateral movement.

AI surface governance and reducing breach risk

CISOs must recognise that AI surface governance and resilience are critical strategic requirements, not compliance exercises. Security policies must govern the use of frontier models, Shadow AI adoption, prompt usage analysis, third-party AI integrations, and agent permissions. CISOs must adopt a shift-left strategy for vulnerability discovery, using the same class of AI-powered tools, i.e., frontier AI models, to uncover the attack surface adversaries could exploit. At the same time, organisations should prepare operationally for AI-enabled breaches: tabletop exercises, AI-red-team simulations, supply-chain compromise scenarios, and incident response plans that assume adversaries can adapt dynamically during an intrusion. The key mindset shift is that frontier AI models are accelerants that reshape the speed, scale, and asymmetry of cyber conflict.

Rapid response assuming AI speed disclosure

The window between vulnerability discovery and exploitation is narrowing. CISOs must understand patch and response process needs and assume that a critical vulnerability may be weaponised within 24 hours of disclosure, or even sooner. Relying on slow patch cycles, manual triage, or periodic security reviews is not viable when adversaries can automate reconnaissance, weaponisation, and exploitation at machine speed. The time demands rapid-response security models that include pre-positioned response playbooks, AI-assisted prioritisation, and resilient architectures capable of quickly containing compromise. In practice, CISOs must assume that once a weakness becomes visible, AI-enabled adversaries can rapidly operationalise it before traditional defences can react.

Reshaping privileged access for AI entities

We are witnessing the evolution of AI solutions that use active agents to interact with APIs, infrastructure, workflows, and enterprise data. CISOs must reshape the privilege-access model for dynamic AI entities, such as agents. Organszations require tightly scoped, identity-aware, and time-bound access models tailored to the AI entities accessing frontier AI models. This means applying zero-trust principles to AI agents, continuously validating their actions, monitoring behavioral deviations, and enforcing granular controls over which data, systems, and operations they can access. With the advent of frontier AI models and AI agents, privileged access management is no longer just about securing human administrators; it is about governing machine-driven entities operating at scale and speed.

The need of the hour: CISO mindset shift

CISOs' practical line of thought: stop planning for the attacker you knew and start planning for the attacker that frontier models enable. That attacker is faster, more contextually aware, more persistent, and more scalable than anything the security industry has faced. CISOs who adapt most quickly to manage the AI attack surface will lead enterprise security in the frontier-model era. Those who treat this as an incremental update to existing frameworks will find that the gap between their defenses and the threat has quietly become insurmountable. CISOs need to internalise this speed asymmetry before building any response strategy.