AI agents shift identity security from protecting accounts to protecting trust

Saviynt says AI agents, machine identities and autonomous workflows are exposing blind spots in traditional identity security, forcing enterprises to rethink governance, visibility and ownership as attackers increasingly target trusted digital identities.

Enterprise identity security is undergoing a structural shift as AI agents, machine identities and autonomous workflows become embedded across business operations, expanding the attack surface far beyond traditional user accounts and credentials.

Speaking to CRN India, Saviynt’s SVP & managing director, India & SAARC, Nithin Varma, said attackers are exploiting trusted machine identities and AI-driven workflows rather than relying solely on compromised employee accounts.

The shift, according to him, is forcing enterprises to rethink identity security around continuous governance, visibility and accountability instead of traditional account-centric controls.

Rather than simply amplifying existing cyber risks, Varma argued AI agents are creating an entirely new identity attack surface that organisations remain structurally unprepared to secure.

"Attackers are now hacking trust models, not just accounts," Varma said.

This changes how identity security needs to operate as enterprises deploy growing numbers of AI agents across customer service, software development, analytics and business automation.

Unlike traditional user accounts, AI agents continuously communicate with enterprise applications, cloud platforms, databases and APIs, often performing thousands of automated actions without direct human intervention.

Varma said this “creates an identity attack surface” that conventional monitoring tools struggle to understand.

He explained that compromised AI agents can execute large-scale data exfiltration or privilege abuse while appearing as legitimate system-to-system activity, making attacks significantly harder to identify using human-centric monitoring models.

AI agents also introduce new challenges around "shadow permissions" and entitlement sprawl, where identities accumulate access privileges across multiple applications without sufficient visibility or governance.

According to Varma, security teams today often lack the observability needed to understand how AI agents interact with SaaS applications, enterprise platforms and internal large language models.

Machine-speed AI exposes governance gaps

The speed at which AI operates is creating another challenge for enterprises.

Traditional access approval workflows were built around human decision-making, where managers could review and approve requests over several hours or even days.

AI agents, however, operate at machine speed.

According to Varma, organisations frequently bypass traditional approval processes by granting AI agents permanent privileged access simply to maintain operational efficiency.

While that approach reduces workflow delays, it also expands enterprise risk by creating standing privileges that contradict just-in-time access principles.

He argued that governance models must therefore evolve from periodic approval mechanisms into continuous risk-based access controls capable of adapting as AI workloads change.

Technology is not the only challenge. Governance ownership remains equally immature.

Varma said AI agents are frequently deployed by DevOps and data science teams without entering formal identity governance processes, leaving enterprises without clear accountability for how those identities are created, governed and eventually retired.

"In most Indian enterprises today, accountability is still missing," he said.

Rather than treating AI agents as technical assets, organisations need to recognise them as first-class identities with clearly defined ownership spanning security, IT, legal and business functions.

Without that governance structure, enterprises risk creating unmanaged identities that accumulate privileges without continuous oversight.

Identity becomes the new security perimeter

Varma also warned against assuming secure cloud infrastructure automatically delivers secure AI deployments.

According to him, one of the most dangerous misconceptions among enterprises is believing that AI systems are protected simply because they operate inside trusted cloud environments.

Instead, identity has become the primary security control as attackers target trusted digital pathways rather than external infrastructure.

He pointed to rising credential-based attacks and sophisticated phishing campaigns as evidence that adversaries increasingly seek legitimate identities capable of moving laterally across enterprise environments.

"The most dangerous assumption is that standard cloud security is enough to protect AI," Varma said.

Looking ahead, Varma believes future AI-driven breaches are unlikely to expose weaknesses in traditional perimeter security alone.

Instead, organisations will regret failing to establish unified identity governance across both human and non-human identities before AI adoption accelerated.

According to him, future audit failures will stem from identity silos, limited observability and entitlement sprawl rather than shortcomings in firewalls or endpoint controls.

As enterprises expand the use of AI agents across critical business processes, continuous visibility into every identity interacting with enterprise systems is likely to become as important as securing the applications themselves.

For channel partners, that evolution creates opportunities to help customers establish unified identity governance frameworks capable of continuously monitoring, governing and securing both human and machine identities as AI becomes embedded across enterprise environments.