Ten things to understand when using agentic AI applications – P0 Security
How security, identity, and governance practices must evolve for autonomous software agents
Agentic AI applications — from copilots to infrastructure automation bots — are no longer passive assistants. They’re autonomous software actors executing real-world actions: managing cloud resources, triggering builds, modifying secrets, and more.
Their behavior is dynamic. Their access footprint is not: shared tokens, long-lived credentials, and hardcoded service accounts.
As organizations begin to operationalize these capabilities, new questions emerge: What can these agents access? Under whose authority? How are they governed? These are Non-Human Identities (NHIs)—and most are unmanaged, unmonitored, and overprivileged.
This isn’t a scary piece. It’s a pragmatic guide to the operational, architectural, and identity implications of deploying agentic software—grounded in current security research and enterprise guidance.
1. Agentic AI ≠ traditional automation
Traditional automation tools—like Terraform scripts or Jenkins jobs—follow fixed logic and predictable triggers. Agentic AI applications, by contrast, introduce autonomy: they plan, adapt, and execute dynamically. As Stanford’s 2023 Foundation Model report explains, LLM-powered agents can interface with APIs and generate actions that developers may not anticipate.
That autonomy increases unpredictability—and expands the blast radius of any misconfiguration, over-permissioning, or governance failure.
2. Every agent is a non-human identity — but rarely managed like one
Gartner’s 2025 IAM Planning Guide reminds us that IAM must cover all identities—human and non-human. Yet most identity programs still focus on workforce users. Agentic systems often run under shared tokens, hardcoded secrets, or long-lived service accounts. Few are assigned individual identities, and even fewer are governed through rotation, audit, or expiration.
That makes them invisible to standard IAM reviews—and creates a governance blind spot.
3. Nonhuman identity sprawl Is a real risk
Gartner’s 2024 Market Guide for IGA confirms what many security teams are already seeing: machine identities outnumber humans, and their growth is accelerating. From agents and bots to CI/CD tools and automation services, these NHIs are created rapidly—and retired rarely.
Without automated lifecycle controls, ghost agents live on with lingering access. Manual cleanup and spreadsheet audits aren’t enough.
4. Least privilege doesn’t mean least risk
“Read-only” isn’t always low-risk. Agents with log access can scrape credentials or sensitive data. Others may have write privileges across environments, justified by convenience rather than necessity. Risk isn’t about assigned roles alone—it’s about effective access in context.
Gartner recommends access graph modeling and risk scoring based on privilege scope and blast radius—not just static policy reviews.
5. API and CLI access are the new front doors
Agents don’t log in through GUIs. They use tokens, API keys, and federated service principals—often long-lived and poorly scoped. Gartner’s PAM Magic Quadrant highlights how many breaches stem not from credential theft, but from improperly governed machine access.
Secrets management posture, expiration policies, and automated token rotation must become core parts of the access review process.
6. Emergent behavior creates unpredictable paths
Agentic workflows often chain together multiple actions—triggering webhooks, invoking downstream services, modifying infrastructure. This behavior creates multi-hop execution graphs that blur traditional privilege boundaries.
As Stanford CRFM notes, transitive access in agentic systems raises new challenges for access control. Addressing it requires runtime observability and access path modeling—not just static role analysis.
7. Agent output can be a security decision
When a Copilot-style assistant suggests an IAM policy change—or a Terraform edit—it’s making a governance decision. If that suggestion is auto-approved or committed, it becomes an execution path.
NIST’s 2023 draft AI RMF warns: AI outputs that modify infrastructure, configuration, or access must be subject to change control and logging, just like human-authored changes.
8. Governance ≠ just reviews — it’s remediation too
Dashboards and alerts can identify overprivileged agents—but what happens next? Mature governance means the ability to act: rotate a key, decommission a role, expire a token, or revoke agent access automatically.
Gartner emphasizes actionable outcomes as the real measure of governance maturity—especially for non-human actors that operate at machine speed.
9. Continuous context matters
Access decisions can’t be static. An agent allowed to deploy in a dev environment may be dangerous in prod. Context-aware enforcement—based on environment, time, source, behavior, or request type—is becoming table stakes.
Just-in-time elevation, adaptive access, and contextual approvals aren’t just human IAM features—they’re essential for managing machine actors as well.
10. Bring AI security into identity architecture now
This isn’t a future risk—it’s a live one. Gartner, NIST, and Stanford all agree: agentic AI systems are rapidly becoming core infrastructure. They must be governed like any other privileged actor—with identity assignment, policy-based access, credential lifecycle controls, and full auditability.
Organizations that treat AI agents like real identities—and govern them accordingly—will be the ones best positioned to move fast without breaking trust.
Final Word
Agentic software isn’t a security threat. But it is a new class of actor that acts on your behalf, across environments, at machine speed. The organizations that thrive will be the ones that extend zero trust and least privilege to the machines acting on their behalf — without slowing them down.
