Last updated on September 21st, 2026 at 08:11 am
AI agents are catching up quickly – even quicker than the security models that organizations constructed to enable the usage of humans. And the cracks are showing.
Ask any security team operating an agentic workflow, and the story will be the same: an agent is running on an agentic token that is cloned, a shared service account that is shared across dev, staging, and prod, and which includes a static API key that is not expired and owned by no one. These aren’t edge cases. As the OWASP Top 10 list of Agentic Applications 2026 notes, over-privileged machine identities and long-lived shared secrets lie at the base of most agentic risk exposures – including the goal hijacking risk to the rogue agent behavior risk.
The least-privilege principle for AI agents is not new. However, using it correctly- with limited permissions, agent identities, limited access based on time, and a clearly defined blast radius is where most teams continue to go on a case-by-case basis.
This article dissects how it works, the anti-pattern appearance, and what a good setup across the entire stack looks like.
Table of Contents
The Anti-Patterns Killing Agentic Security Right Now
Agents Borrowing Human Admin Tokens
This is, I believe, the most frequent error I have seen in real deployments. One developer ends up spinning up an agent whose operations require it to invoke an internal API, and using their own OAuth token or a long-lived teammate administrator credential. It takes effect instantly – and that is the trouble.
Permissions assigned to human admin tokens are tied to a person’s entire role, not an operation. When an agent takes one, it gains read/write access to things it has no business touching. When such an agent might be manipulated later in time, such as by a prompt injection, through a tool that is compromised, or through a corrupted memory state, such an agent may do something inconsistent with the intent of any individual.
Shared Service Accounts Across Environments
One of the clearest indicators of a failed privilege model is non-human identification, as discussed in the OWASP NHI Top 10 flags. When the same service account authenticates agents in dev, staging, and production, a violation in one environment is a violation in the other two. Cascade failures are not theoretical; they are the rational outcome of credential reuse.
Static Keys With No Expiry
The agentic counterpart to locking your front door with an unlocked front door is using long-lived API keys and non-rotating static secrets. The FINOS AI Governance Framework MI-18 control states that non-expiring, non-changing credentials are a structural risk that undercuts all other downstream controls.
I had a chance to practice that even teams that are otherwise strong in IAM habits of treating human users heedlessly neglect key rotation of machine identities – merely because there is no process to drive it.
Per-Agent Identities: Every Agent Needs Its Own “Persona”
What a Per-Agent Identity Actually Means
The fix begins before an agent runs a single task. Register this under each agent, with a distinct client/app registration, service principal, or service account; a principal with a known owner; and a purpose for which the registration was created.
This isn’t just bureaucracy. It underpins all other features: scoped permissions, audit trails, revocation, and rotation. In its absence, you end up entering the service-account-shared-01 called the payments API, rather than invoice-processing-agent called the payments API, triggered by workflow X, on behalf of user Y.
The Apono blog on agentic AI security explains that existing static privilege models designed for human users fail in agentic contexts, since agents do not act like humans. They run, burst-call APIs, operate across sessions, and have no natural log-off point.
Ownership and Purpose as Security Controls
At registration, every agent identity must answer two questions: who owns this identity, and what is its purpose? Ownership brings accountability because, for rotating credentials, reviewing access, and decommissioning the identity, someone is held responsible when an agent retires. Purpose imposes the restriction that makes least-privilege design tractable.
A more general problem of managing non-human identities at scale, in terms of their provisioning, monitoring, and revocation, is an area that I explored in more detail in the Identity Crisis – Securing Non-Human Identities for AI Agents, in which I describe the NHI governance layer that lies beneath all of the following in this document.
Scope Design: Permissions That Match the Task, Not the Team
Task-Scoped Permissions
The scope design is straightforward: an agent must have access to the APIs and resources needed for the particular workflow. Nothing hereditary, nothing adopted, nothing in case.
In practice, this translates to workflow-based, not agent-based, permissions. A document-summarization agent must have read access to a document store. It does not require write access, delete access, or access to neighboring services. I’ve observed that most teams initially resist this level of granularity because it feels like overhead. Still, the overhead drops substantially once you settle into a common pattern of scoping at workflow design time rather than debugging access problems in production.
Cerbos comes in handy here: policy permissions are policies on actions, not policies on identities. The agent’s identity then requests to perform a particular action in a particular environment, and the policy level responds by granting or denying it. It raises the question of who this agent is and what it is permitted to do at this point.
Environment-Scoped Access
The Dev, staging, and Production domains are separate permission domains because they share no credentials. This might seem self-evident, but the advice provided by Cloud Security Alliance regarding the containment of blast radius reminds us how frequently the limits to environments are violated in practice – most of the time someone has to do something fast and cross a boundary with a credential, once only.
Environment-scoped access means an agent in a dev environment could be completely compromised, and the attacker would still have nothing useful in production. Environment isolation happens at the identity layer, not just the network layer.
Time-Bound and Just-in-Time Access: Stop Trusting Long-Lived Tokens
Short-Lived Tokens as a Default
One change an organization can make to maximize leverage is moving from long-lived to short-lived tokens. Something that expires in 15 minutes has a much different risk profile than something that lasts a year. The intervention period is narrow, even in the context of theft or abuse.
Self-service tokens are also short-lived, which supports healthy operational positioning. When your agent cannot operate without refreshing its credentials, you have to start the workflow by integrating proper credential management, instead of embedding a secret key and forgetting about it.
Just-in-time authorization via PAM
Standing permissions aren’t the right model for sensitive operations, especially when writing to production databases, calling financial APIs, modifying infrastructure, etc. It improves just-in-time (JIT) access by using a Privileged Access Management (PAM) system.
JIT elevation means the agent requests elevated access only when needed; the PAM system issues a time-limited role for that specific action, and the elevated access is automatically withdrawn after the window closes. Strata.io analysis of least privilege in agentic AI: Technically, the argument is that JIT access is the only model to scale gracefully to an agent becoming more autonomous, since it decouples the identity base of the agent from any given time and authorization that may be temporarily conferred on the agent, and the audit trail is kept clean.
My Experience has demonstrated that latency is the primary contribution point in this situation – JIT flows introduce a round trip. The practical way out is to model workflows so that sensitive, elevated operations are batched or pre-authenticated in a specification session window, rather than sending a new JIT request with each API call.
Blast Radius Control: What Happens When Something Goes Wrong
Mapping OWASP Agentic Risks to Scope Controls
According to the OWASP Top 10 of Agentic Apologetic, 2026 newcomers, there are several categorizations of risks where least privilege is a direct control measure:
ASI01 — Goal Hijacking: This occurs when an agent is tricked into achieving unwanted goals (through prompt injection or misleading context); scope restrictions control the extent of its harm. Even when an agent’s objectives have been redefined, an agent with read-only access to a given document store cannot steal data outside a database it cannot access.
ASI02 Tool Misuse: A compromised agent with access to wide tool sets is more hazardous. Task-scoped permissions limit an agent’s tool access to only what the legitimate workflow requires, reducing the attack surface available to attackers.
ASI03 Identity and Privilege Abuse: Per-agent identities that are owned or documented are a much harder target, since they are much less prone to abuse without being detected as anomalous. Shared identities don’t show up in audit logs, whereas dedicated agent identities do.
ASI10 – Rogue Agent Behavior: Time-bound tokens come in handy in this case. An agent intended to be a workflow will expire its credentials and be unable to perform any work, serving as a circuit breaker that resthat restricts autonomous out-of-bounds actions.
The Blast Radius Formula
Blast radius in agentic systems depends on three variables: the width of an identity’s permissions, the duration of those permissions, and the environments an identity can access. This is encapsulated in the Lumos model of blast radius in cybersecurity, where any unit that narrows scope, reduces token lifetime, or distances environments directly lowers the blast radius of a particular compromise.
The math is straightforward. A long-lived, cross-environment, and administration (admin) credential has a blast radius that covers the whole organization. The blast radius of an agent with 15-minute,d task-scoped tokens confined to staging is determined by a handful of API endpoints. That’s the design goal.
What “Good” Actually Looks Like: A Practical Checklist
Forming a list of teams building or auditing the agentic privilege models, this is a working reference:
Identity Layer
- Every agent possesses a service account, service principal, or client registration.
- Someone owns each identity, and its purpose is documented.
- Cross-agent or cross-environment credentials are forbidden.
Scope Layer
- Permissions are set at the workflow level, not the agent level.
- Each task has set APIs and resources plus actions, of which only a subset are accessible.
- The dev, staging, and production domains are independent permission domains with no credential cross-over.
Temporal Layer
- The default token TTL is 15-60 minutes, based on workflow sensitivity.
- Exception and frequent review are necessary for long-lived tokens.
- JIT elevation is used for sensitive operations via a PAM system, with automatic expiry.
Monitoring Layer
All agent API calls are recorded under a specific agent identity, not a shared account.
Anomaly detection is tuned to the agent’s behavior patterns, not the human session.
The monitored and alerted events include credential expiry and rotation.
My Take: Least Privilege Isn’t a One-Time Configuration
The greatest myth I have experienced is the mistake of viewing least privilege as a configuration activity – one that you develop at deployment and forget once complete. It is a continuous operations discipline in agentic systems.
Agents evolve. Workflows change. New tools get added. With every change comes the possibility of scope creep, credential drift, or a new anti-pattern creeping in. Organizations that find it right will consider privilege review through the agent lifecycle, not as a checkbox when deploying agents.
The Obsidian Security summary of AI agent security repeats a point that deserves reiteration: the threat model of AI agents is not the same as that of human users, and controls must reflect that. Time-based access, agent identities, task-specific permissions, and intentional blast-radius design are not optional hardening measures. They’re the baseline.
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