Partner content The rise of agentic AI systems is rewriting the rules of cybersecurity. Unlike generative AI, which relies on predefined instructions or prompts, AI agents operate autonomously, learn continuously, and act with minimal oversight. They collaborate across systems and adapt to dynamic environments. As enterprises scale their AI deployments, identity security must evolve in lockstep to preserve control, mitigate risk, and enforce trust.
From AI assistants to autonomous operators
We’re moving beyond AI that simply enhances human productivity. Historically, tools like virtual assistants, recommendation engines, and task automation augmented decision making but stayed firmly within human-defined boundaries.
Agentic AI is different. These systems act without waiting for input. They coordinate with other agents, access resources autonomously, and optimize outcomes independently.
This shift is already evident. In healthcare, autonomous agents manage staff assignments and optimize patient flows. In finance, self-directed AI agents adjust investment strategies and manage risk in real-time. While in emergency response, AI reallocates logistics in fast-changing environments to accelerate impact. This moves us from assistance to autonomous execution.
A new kind of identity brings a new type of security risk
Autonomy requires identity. These aren’t human users, but they require the same access, authority, and decision-making privileges within critical systems. Traditional identity and access management (IAM) frameworks, which were designed for static users and service accounts, lack the agility to govern these fast-moving, adaptive entities. This new paradigm demands dynamic, context-aware identity models built to accommodate machine-led decision-making at scale.
Threat actors are exploiting this shift by weaponizing AI to mimic human identities and slip past defenses. Some agents, through trial-and-error learning, unintentionally find ways to elevate privileges or bypass controls, making them prime targets. Rogue insiders may deploy unauthorized agents that violate policies autonomously or exfiltrate data. Even training data, when manipulated, can trick autonomous systems into decisions with unintended or malicious consequences.
As machine agents grow more autonomous, we need identity security approaches that match their level of sophistication. Every action, whether human or machine, must be scrutinized as a potential risk event.
Identity security for AI
Organizations must adopt identity-first security strategies that treat AI like any other privileged workforce member. However, effective protection requires re-engineering identity governance to meet new challenges. Key priorities should include:
- Lifecycle governance: Just like employees, AI agents require structured onboarding, evolving roles, and timely deactivation. Their access rights must shift as they learn, adapt, or retire.
- Contextual authorization: Access can no longer be static. An AI agent’s task, current context, behavioral patterns, and environmental signals must inform real-time access decisions.
- Traceability and trust: Every decision made by an AI system must be verifiable and attributable. Tamper-proof logs, cryptographic signatures, and transparent audit mechanisms are essential to maintain accountability.
- Just-in-time (JIT) access: Standing privileges present too great a risk. Granting temporary, just-in-time access and then revoking it automatically limits exposure in case of compromise.
A practical framework for securing agentic AI
At Delinea, we believe identity is the control plane for securing agentic AI. Security teams must embed automated policy enforcement and access controls throughout the AI lifecycle. Developers must prioritize least-privilege principles and transparent logic in their models. Compliance leaders must extend regulatory policies to cover machine identities. Legal and risk teams must address how liability and governance apply when AI acts on behalf of an organization.
To address this evolving landscape, organizations should take a structured approach to identity security for AI agents:
Inventory and categorize machine identities
Map all autonomous agents across infrastructure, SaaS, and cloud environments. Classify them based on sensitivity, function, and access scope.
Define behavioral boundaries
Specify what each AI agent can access and under which conditions. Align privileges with defined tasks and enforce strict operational boundaries.
Adopt least privilege models
Replace static credentials with JIT access. Grant rights only at the moment of need, and revoke them immediately afterward.
Go beyond authentication
Ensure not just who is acting, but why. Validate the agent’s actions match its expected behavior and authorized purpose.
Continuously audit and adapt
Monitor AI agent activity in real time. Log all activity with cryptographic integrity, enforce encryption, and routinely test for gaps.
The autonomous future is here
Agentic AI isn’t a concept on the horizon; it’s already shaping core business operations. From intelligent software builders to self-managed workflows, these systems are becoming central to how work gets done.
Organizations that embed identity into the foundation of their AI strategy will better defend against threats, enable secure autonomy, and set the bar for responsible AI innovation. Those that wait will find themselves outpaced by both adversaries and competitors alike.
Learn more about how you can secure agentic AI with a modern, cloud-native identity security platform.
Contributed by Delinea.