How to secure AI agents: a runtime-first approach

Organizations are deploying AI agents faster than they can  redesign security frameworks around them, leaving many without a clear strategy once agents reach  production. Every AI agent operates as  a non-human identity capable of retrieving enterprise data, invoking APIs, and taking autonomous action using valid  credentials. Traditional security models built around human access patterns were never designed for software operating independently with trusted permissions. 

The core challenge is no longer just controlling access at the perimeter;  it is ensuring trusted agents stay within their intended purpose after authentication. True agentic security requires moving beyond static identity management and model guardrails to establish continuous runtime visibility, where prompt manipulation manifests as a real-world action. 

At a glance

• AI agent security depends on three complimentary control layers: identity, runtime, and detection.
• Least-privilege credentials reduce an agent’s potential blast radius before deployment.
• Runtime monitoring detects misuse that identity controls alone cannot prevent and supports guidance from OWASP LLM06: Excessive Agency and the NIST AI RMF Manage function.
• Deception-based detection validates intent by exposing unauthorized runtime activity through canary tokens, honeytokens, honeypaths, and deceptive services.
• Acalvio Agentic AI Runtime Protection deploys preemptive runtime controls without requiring instrumentation of the AI model itself.

Runtime-first AI security continuously validates an AI agent’s actions after authentication rather than relying solely on initial credential checks or pre-deployment model guardrails.

This strategy coordinates three complementary layers: identity, runtime, and detection. The first layer establishes exact operational boundaries before an agent ever interacts with enterprise data or systems.

Every AI agent requires a dedicated, purpose-built identity before connecting to enterprise systems. Too often, organizations accelerate deployments by reusing existing service accounts or assigning overprivileged API keys for convenience. These shortcuts dramatically expand the potential blast radius if an agent is manipulated through prompt injection, context poisoning, or compromised integrations.

Identity controls define what an agent is permitted to access and execute before runtime begins. Beyond initial authentication, their primary role is to restrict the damage if preventative controls fail.

Five foundational identity controls

Every deployment should include five foundational identity controls:

• Assign dedicated, least-privilege service accounts for each AI agent.
• Issue short-lived credentials whenever possible to reduce credential exposure.
• Store secrets in approved secrets management platforms rather than application code or configuration files.
• Seed canary and honeytokens within credential stores to detect unauthorized credential access.
• Maintain an inventory of non-human identities so every agent credential is tracked, reviewed, and governed.

Identity security reduces risk before an AI agent begins operating, but it cannot determine whether an authenticated agent continues acting within its intended purpose. That requires runtime visibility.

Runtime security builds directly on identity controls. Once an agent authenticates, security shifts from validating access to continuously monitoring behavior.

Table 1: Three layers of AI agent security

Authentication establishes identity. Runtime monitoring validates behavior. Traditional security monitoring was designed Free human access patterns built around periodic logins, predictable workflows, and human-speed interactions.. AI agents behave differently. They continuously retrieve information, invoke APIs, access enterprise resources, and make decisions in seconds or milliseconds.

Because of this, security teams must evolve how they monitor runtime activity. Instead of relying primarily on user behavior analytics (UBA), runtime monitoring should focus on whether an agent operates within its expected scope.

Monitor behavior, not just authentication

Effective runtime monitoring includes:

1. Logging full agent-to-API execution chains to detect out-of-sequence or unexpected function calls.
2. Monitoring data retrieval patterns to flag unauthorized access to sensitive repositories.
3. Detecting lateral movement attempts beyond approved network or application boundaries.
4. Identifying privilege escalation attempts when an agent uses valid credentials outside its defined scope.
5. Tracking abnormal workflow velocity or execution context across integrated enterprise systems.

Honeypaths and deceptive navigation paths extend runtime visibility throughout enterprise environments. Any AI agent that interacts with those paths provides a high-confidence indicator that it has exceeded its intended scope.

Where traditional SIEM correlation assumes authentication is the beginning of trust, AI agents invert that assumption. Authentication simply marks the beginning of continuous validation.

Table 2: How AI agent runtime monitoring differs from traditional user monitoring

Deception is the detection layer that works regardless of how an agent was manipulated, whether through prompt injection, poisoned retrieval data, compromised plugins, or stolen credentials. Rather than inferring intent from behavior, deception validates it through interaction: “Why did anything interact with an asset that no legitimate process should ever access?”

Effective deployments seed canary and honeytokens in every data store an agent can reach, stand up decoy API endpoints alongside production services, and place deceptive credentials in accessible secret stores. Because none of these assets serve any legitimate purpose, an agent that touches one provides a high-confidence indicator that it has exceeded its intended scope.

Unlike traditional honeypots, which are static, manually configured, easy to fingerprint, and quick to go stale, modern deception platforms deploy and refresh these assets automatically, so coverage scales as agents, APIs, and identities change around them.

Agentic AI Runtime Protection is the purpose-built implementation of this layer, extending deception across AI agent workflows without requiring instrumentation of the underlying AI system.

Table 3: Runtime security capabilities work together

Acalvio’s 360 Deception complements IAM, SIEM, and EDR by providing high-confidence runtime detection that validates intent after authentication rather than replacing existing security investments.

Modern security frameworks increasingly emphasize continuous runtime validation for AI systems. A runtime-first security model aligns directly with both industry standards and emerging AI governance guidance.

• OWASP Top 10 for LLM Applications: Addresses core vulnerabilities such as Prompt Injection (LLM01), Sensitive Information Disclosure (LLM06), and Excessive Agency (LLM08). While identity controls limit initial permission scope, 360 Deception detects active exploitation when an agent is manipulated into taking unauthorized actions.

The NIST AI Risk Management Framework specifically aligns with the manage function by establishing continuous monitoring, risk mitigation, and automated response controls during model execution.

Table 4: Mapping runtime-first security controls to OWASP and NIST

Four common pitfalls in AI agent security

1. Relying solely on identity controls for post-authentication safety Identity controls manage permissions before an agent connects, but they cannot verify whether an authenticated agent continues acting within its intended scope. Fix: Pair least-privilege identities with continuous runtime monitoring and active deception.
2. Deploying agents with administrative permissions Broad permissions simplify initial deployment but drastically expand the blast radius if an agent is manipulated or hijacked. Fix: Create dedicated service accounts scoped strictly to the minimal privileges required for each task.
3. Assuming model guardrails eliminate operational risk Prompt filtering and safety guardrails evaluate inputs and outputs at the model level, but they cannot observe downstream runtime actions across enterprise systems. Fix: Monitor runtime execution independently of the AI model.
4. Skipping adversary emulation Organizations routinely test AI functionality and task performance, but rarely simulate agent manipulation, context poisoning, or out-of-scope execution. Fix: Validate runtime controls and active tripwires using realistic attack scenarios prior to production deployment.

Together, identity, runtime monitoring, and deception-based controls answer distinct security questions across the AI agent lifecycle. Moving to a runtime-first approach ensures organizations can safely deploy agentic workflows without sacrificing visibility, control, or speed.

Table 5: Identity and runtime security responsibilities