Earlier in 2025, an AI agent named Claudius made headlines when it insisted it was human, promising to deliver products in “a blue blazer and red tie.”
Quirky? Sure. But beneath the strange admission sat a more important truth: today’s AI agents aren’t just chatbots with puppet-like ambitions.
They’ve evolved into actors with real credentials, access, and autonomy.
For enterprises, autonomous AI agents can deliver undeniable value, updating customer records, generating reports, moving files between systems, and even provisioning cloud resources — all at machine speed. And companies are adopting them just as rapidly.
According to PwC’s 2025 “AI Agent Survey,” 79% of companies already deploy agentic AI, with two-thirds reporting measurable productivity gains. Three-quarters of executives even said AI agents will reshape the workplace more than the internet.
However, the very features that make AI agents so powerful — speed, autonomy, and deep integration into enterprise systems — also amplify their risks. Machine identities already outnumber human identities by a wide margin in modern environments, and many organizations still lack mature controls for securing non-human and AI-driven access. Palo Alto Networks’ guidance on machine identity, machine identity security, and identity security reinforces this point: unmanaged non-human access creates a fast-growing attack surface.
When you add AI agents into the picture, thousands of new digital workers can operate with elevated access and act without constant human oversight. The risks are significant, but they can be managed if organizations view AI agents for what they really are: privileged machine identities.
The overlooked risks of privileged machine identities
AI agents are commonly given elevated standing access and are specifically designed to act independently. But the gap between adoption and security is already stark.
Many organizations still define “privileged” identities too narrowly, focusing on human users while underestimating how often machine identities touch sensitive systems and data. That blind spot matters because AI agents are not passive integrations. They can take action, chain decisions, and interact with business-critical systems in real time.
These realities paint a troubling picture of overlooked privilege risk in machine identity security. They show why organizations must recognize AI agents as a new form of privileged identity — human-like in capability, but operating at the unprecedented volume, variety, and velocity of machines.
Why AI agents are privileged machine identities
AI agents inherit many of the same risks as other machine identities: excessive permissions, stolen credentials, and secrets leakage — all familiar challenges for security teams.
What’s different is the non-deterministic, human-like behavior of agentic AI. In some cases, authentication through OAuth tokens and consent flows may work when AI agents perform specific tasks on behalf of a person. But that will cover only a narrow slice of use cases.
Autonomous AI agents and multi-agent systems will continue to become more common, and those more complex use cases will function much like cloud-native workloads, relying on machine identities such as secrets, API keys, and certificates. To deliver real value, AI agents also require elevated access across SaaS platforms, databases, and cloud environments. Non-human entities need verifiable identity, tightly scoped access, and continuous control.
Because while they do read data, they also behave dynamically, making decisions in real time to find the best way to execute assigned goals. While these levels of autonomy allow AI agents to reason and operate with minimal human oversight, they can also enable cascading failures with relative ease.
Validating AI agent risks with OWASP and emerging agentic AI threat models
The security community is still codifying frameworks for agentic AI security, and the details will continue to evolve alongside the technology itself. OWASP’s Top 10 for LLMs is one early effort to systematize AI risk models, and several of those risks apply directly to AI agents. Palo Alto Networks has also published guidance on agentic AI security, agentic AI governance, and Unit 42 research into emerging AI-agent threats.
For example:
- Privilege abuse: An AI agent with excessive permissions approves a financial transfer or exposes sensitive records.
- Tool misuse: Attackers manipulate agents into misusing legitimate integrations, turning business functions like CRM access or cloud storage into attack vectors.
- Memory poisoning: Threat actors feed malicious inputs into an agent’s context so that its decisions become skewed over time, leading it to produce flawed outputs or act in ways that undermine security.
These are familiar identity security challenges magnified by AI agent capabilities.
While agentic AI is still an emerging space, one principle is already clear: identity must sit at the foundation of agentic AI security.
Identity security guardrails for the autonomous AI workforce
How should organizations secure these new workforce entities?
The answer is to extend identity-first security practices to AI agents, treating them the same way they would other privileged machine identities through a zero trust, identity-first approach.
That means:
- Know your agents: As with human employees, agents must be rigorously discovered, onboarded, and decommissioned. This helps prevent unmanaged or orphaned AI agents from operating in the shadows.
- Monitor behavior dynamically: Real-time policy enforcement, session monitoring, and isolation can help detect anomalies and rogue behavior as it happens, keeping pace with the speed of agentic AI activity.
- Control access with precision: Just-in-time (JIT) access, zero standing privileges (ZSP), and scoped entitlements can reduce over-permissioning and limit the potential blast radius.
The goal is not to slow AI agents down with cumbersome oversight, but to confidently enable autonomy by deploying effective guardrails.
Scaling identity-first security for AI agents
Identity-first guardrails only work if they scale alongside AI agents themselves. That means moving beyond static rules to dynamic, context-aware controls: privileges that flex in real time based on roles, context, and intent.
For today’s enterprise, it also means securing human, machine, and AI agent identities with similar levels of rigor.
How to safely adopt agentic AI without slowing innovation
AI agents are already on the job in many organizations, acting like human employees and reshaping how enterprises operate. They’re new workforce entities that behave as privileged machine identities, operating at enterprise scale and machine speed. They amplify risks that security leaders already understand, while introducing new twists such as hallucinations, tool abuse, and rogue behavior.
The security frameworks for this space may still be developing, and the details will continue to evolve. But identity security is the foundation organizations must build on now. Those that extend proven controls to AI agents can put themselves in a far stronger position to embrace new levels of autonomy and productivity with confidence.
To explore this topic further, see Palo Alto Networks resources on agentic AI security, machine identity security, and Unit 42’s research on AI-agent threats.