AI Agent Security Best Practices: 5 Risks and 10 Controls

What Is AI Agent Security?

AI agent security is the set of practices, controls, and technologies used to protect autonomous AI systems from threats and misuse. Unlike traditional software, AI agents can interact with users, access data, and execute actions in dynamic and unpredictable ways. This flexibility increases the attack surface and introduces new types of vulnerabilities, such as manipulation of language inputs, unauthorized tool use, and memory poisoning.

As AI agents become more integrated into business operations, their security must be managed with the same rigor as any critical IT system. Securing AI agents requires an identity-first approach combined with strict runtime guardrails and continuous behavioral monitoring.

Core best practices:

Give every AI agent a unique identity: Assign each agent a distinct identity so its permissions and actions can be authenticated, controlled, and audited.
Apply least privilege to agent workflows: Grant agents only the data, tools, APIs, and actions required for their assigned tasks.
Use short-lived, scoped credentials: Issue narrowly scoped credentials that expire automatically to limit the impact of credential theft.
Control script and interpreter execution: Restrict agents to approved interpreters, scripts, commands, and isolated execution environments.
Enforce Deny by Default for agent-executed code: Block code and command execution unless the operation has been explicitly permitted.
Require human approval for high-risk actions: Route sensitive or high-impact operations to authorized humans before allowing execution.
Protect agent memory: Restrict, validate, encrypt, and periodically purge stored agent context to prevent poisoning and data exposure.
Sandbox agent execution: Isolate agent processes and restrict their filesystem, network, and system access to contain compromised behavior.
Log and monitor every important agent action: Record and analyze tool calls, data access, code execution, permission changes, and approvals for suspicious activity.
Continuously review the AI agent execution baseline: Compare runtime behavior with expected activity to identify anomalies, permission drift, and new attack paths.

This is part of a series of articles about agentic AI security.

In this article:

What Are the Biggest Security Risks for AI Agents?
10 AI Agent Security Best Practices

What Are the Biggest Security Risks for AI Agents?

1. Prompt Injection and Agent Hijacking

Prompt injection is a technique where attackers manipulate the language input provided to an AI agent, causing it to behave in unintended ways. This can involve embedding malicious instructions within user input or context, tricking the agent into revealing sensitive information or executing unauthorized actions. As agents increasingly rely on large language models, the risk of prompt injection grows, especially in open-ended or user-facing applications.

Agent hijacking occurs when an attacker gains control over an AI agent’s workflow, often by exploiting prompt injection vulnerabilities or weak input validation. Once hijacked, the agent can be instructed to perform harmful operations, such as altering data, exfiltrating secrets, or abusing connected APIs.

How to address:

Defending against these attacks requires input sanitization, prompt design, and continuous monitoring to detect suspicious activity.

2. Excessive Permissions

AI agents are often granted broad access to systems, data, and APIs to maximize their utility. However, excessive permissions create a significant risk if the agent is compromised. An attacker who gains control of a highly privileged agent can cause widespread damage, including data breaches or operational disruptions. Overprovisioning also makes it harder to track and contain incidents.

How to address:

Limiting permissions to only what an agent needs for its specified tasks is a key security practice. This reduces the potential impact of any compromise and simplifies auditing. Security teams should regularly review agent permissions and workflows, removing unnecessary access to sensitive resources and enforcing the principle of least privilege.

3. Sensitive Data Exposure

AI agents often process sensitive information, such as personal data, proprietary business details, or authentication credentials. If input validation and output filtering are inadequate, agents can inadvertently leak this information in their responses or logs. Attackers may exploit these weaknesses through targeted queries or prompt injection, extracting confidential data without detection.

How to address:

To mitigate this risk, organizations must implement strict data handling policies for AI agents. This includes redacting sensitive information from agent memory and outputs, encrypting communication channels, and restricting data access based on context and role. Regular audits and monitoring are essential to identify and remediate any data exposure incidents promptly.

4. Tool and API Abuse

Many AI agents are designed to interact with external tools and APIs to perform useful tasks, such as sending emails, updating records, or retrieving data. However, if access to these tools is not tightly controlled, attackers can abuse agent capabilities to execute unauthorized actions. For example, a compromised agent might send malicious emails or manipulate business processes through integrated APIs.

How to address:

Securing tool and API integrations requires enforcing strict authentication, authorization, and input validation for every action initiated by an agent. Organizations should maintain allowlists of permitted operations and continuously monitor agent activity for signs of abuse. Any anomalies or unexpected API calls should trigger immediate investigation and, if necessary, containment.

5. Memory Poisoning

Memory poisoning targets the persistent storage or memory of AI agents, introducing false or malicious data that influences future agent behavior. Attackers may inject misleading information through user interactions or compromised data sources, causing the agent to make faulty decisions, leak sensitive information, or perform unintended actions. This attack can be subtle and difficult to detect, as poisoned memory may persist across sessions.

How to address:

Defending against memory poisoning requires validation of all data written to agent memory and mechanisms to detect anomalous or unauthorized changes. Agents should be designed to regularly verify the integrity and trustworthiness of their memory, discarding or flagging suspicious entries. Periodic reviews and reset mechanisms can further reduce the risk of persistent poisoning attacks.

10 AI Agent Security Best Practices

Organizations should consider the following practices to improve their security when using AI agents.

1. Give Every AI Agent a Unique Identity

Assigning a unique identity to each AI agent is foundational for secure management. Unique identities enable precise tracking, auditing, and control of agent actions across systems. This approach supports granular permission assignment, making it easier to enforce security policies and detect anomalous behavior. If all agents share a generic identity, it becomes difficult to attribute actions and respond to incidents effectively.

Implementing unique identities also aids in incident response, as security teams can quickly isolate or revoke access for compromised agents without affecting others. Identity management should integrate with existing directory services and authentication systems, ensuring consistency across human and AI entities. Properly managed identities form the basis for access control and accountability in AI-powered environments.

Key actions:

Assign a separate machine identity to every deployed agent.
Authenticate the agent before granting access to tools and data.
Map each identity to an owner, purpose, and approved workflow.
Revoke identities immediately when agents are retired or compromised.

2. Apply Least Privilege to Agent Workflows

Enforcing the principle of least privilege means granting AI agents only the minimum permissions necessary to complete their designated tasks. This limits the potential damage from compromised agents and reduces the risk of accidental or intentional misuse. By carefully defining agent workflows and access requirements, organizations can prevent privilege escalation and lateral movement within their environments.

Regularly reviewing and updating agent permissions is critical as workflows evolve. Automated tools can assist in identifying unnecessary privileges and flagging deviations from established policies. Least privilege strengthens security and simplifies compliance and auditing by minimizing the scope of agent access.

Key actions:

Grant only the permissions required for the agent’s defined tasks.
Scope access to specific resources, operations, and environments.
Review permissions when workflows or connected tools change.
Remove unused and excessive privileges automatically where possible.

Related content: Read our guide to zero trust security pillars, technologies, and implementation steps.

3. Use Short-Lived, Scoped Credentials

Short-lived, scoped credentials minimize the window of opportunity for attackers to exploit stolen or leaked authentication details. By issuing time-limited credentials with tightly defined access scopes, organizations can reduce the impact of credential compromise. Credentials should automatically expire and require re-authorization for continued use, making it harder for attackers to maintain persistent access.

Implementing credential rotation and revocation policies further strengthens security. Automated systems can detect suspicious credential usage and trigger immediate invalidation, preventing unauthorized actions by compromised agents. Scoped credentials also support least privilege, ensuring agents can only access resources necessary for their current tasks.

Key actions:

Issue temporary credentials instead of persistent secrets.
Scope credentials to specified resources and permitted operations.
Store secrets outside prompts, memory, and agent-accessible files.
Revoke and rotate credentials when suspicious activity is detected.

4. Control Script and Interpreter Execution

AI agents with the ability to execute scripts or code pose a significant security risk if not tightly controlled. Unrestricted code execution can be exploited by attackers to run arbitrary commands, escalate privileges, or exfiltrate data. Organizations must implement strict controls over which interpreters and script types agents can access, limiting execution to pre-approved environments and languages.

Monitoring and logging all script execution events is essential for detecting suspicious activity. Security teams should enforce input validation and output filtering to prevent injection attacks and ensure scripts do not interact with unauthorized resources. Regular reviews of interpreter configurations and execution policies help maintain a secure operational environment for AI agents.

Key actions:

Allow only approved interpreters, runtimes, and script types.
Restrict filesystem, network, and operating system access during execution.
Validate agent-generated commands before they run.
Log scripts, commands, execution results, and errors.

Related content: Read our article about application control software and its key features.

5. Enforce Deny by Default for Agent-Executed Code

A Deny by Default policy ensures AI agents cannot execute code or commands unless explicitly permitted. This approach minimizes the attack surface by blocking unauthorized actions and requiring deliberate approval for each new operation. Deny by Default is particularly important for agents with dynamic or user-driven workflows, where the risk of unexpected code execution is higher.

Implementing this policy involves configuring execution environments to reject unapproved scripts, commands, or API calls by default. Exceptions should be granted only after thorough review and risk assessment. Deny by Default reduces the likelihood of accidental or malicious execution, providing an essential safeguard for AI agent operations.

Key actions:

Block scripts, commands, and executables unless explicitly permitted.
Maintain allowlists for approved operations and execution paths.
Require review before adding new execution exceptions.
Test policies regularly to confirm unauthorized code remains blocked.

6. Require Human Approval for High-Risk Actions

Some actions performed by AI agents (such as modifying critical data, transferring funds, or changing system configurations) carry significant risk. Requiring human approval for these high-impact operations adds a vital layer of oversight and reduces the chances of catastrophic errors or abuse. Human-in-the-loop controls ensure that sensitive actions are reviewed and authorized by responsible personnel.

Approval workflows should be clearly defined and integrated with agent management systems to simplify decision-making without introducing unnecessary delays. Logging every approval and the associated context supports auditing and post-incident analysis. By combining automation with human judgment, organizations can balance efficiency and security for high-risk agent activities.

Key actions:

Define which actions require human authorization before execution.
Set approval thresholds based on financial, security, and operational impact.
Provide approvers with the action, target, data, and risk context.
Record approval, rejection, and override decisions for auditing.

7. Protect Agent Memory

Agent memory often stores context, state, and historical data needed for ongoing tasks. If left unprotected, this memory can become a target for attackers seeking to inject malicious content or extract sensitive information. Implementing encryption for stored memory, coupled with strong access controls, helps prevent unauthorized access and tampering.

Regularly validating the integrity of agent memory and monitoring for unusual changes are essential practices. Memory protection mechanisms should include automatic purging of outdated or unnecessary data to limit the impact of potential exposure. By protecting agent memory, organizations reduce the risk of memory poisoning and data leakage.

Key actions:

Validate and sanitize information before writing it to persistent memory.
Encrypt sensitive memory and restrict which identities can access it.
Track memory changes to identify unauthorized or suspicious modifications.
Expire or delete stored information when it is no longer required.

8. Sandbox Agent Execution

Sandboxing isolates AI agent processes from the underlying system and other applications, containing any damage in the event of compromise. By running agents in restricted environments with limited access to resources, organizations can prevent attackers from escalating privileges or affecting broader system operations. Sandboxing is particularly important for agents that execute code or interact with external data sources.

Effective sandboxing involves setting clear boundaries on file access, network communication, and system calls. Continuous monitoring of sandboxed environments can detect escape attempts or policy violations. Regular updates to sandbox configurations ensure they remain effective against evolving threats and agent capabilities.

Key actions:

Run agent-generated code in isolated, restricted environments.
Limit network connections, filesystem access, and system calls.
Apply CPU, memory, execution-time, and other resource limits.
Destroy or reset execution environments after sensitive tasks.

9. Log and Monitor Every Important Agent Action

Comprehensive logging provides visibility into how AI agents use data, tools, credentials, and connected systems. Logs should capture important events such as authentication attempts, tool calls, API requests, permission changes, memory updates, code execution, and human approvals. Each event should include the agent identity, timestamp, requested action, target resource, and outcome to support investigation and auditing.

Monitoring systems should analyze these events for unusual behavior, such as unexpected tool use, repeated authorization failures, abnormal data access, or actions outside an agent’s normal workflow. High-risk anomalies should generate alerts and support rapid containment, such as disabling an agent or revoking its credentials. Logs should also be protected against tampering and retained according to security and compliance requirements.

Key actions:

Record authentication, tool calls, API requests, code execution, and data access.
Include agent identity, target resource, timestamp, and outcome in events.
Alert on abnormal behavior and repeated policy violations.
Protect logs from tampering and retain them according to policy.

10. Continuously Review the AI Agent Execution Baseline

An execution baseline defines the expected behavior of an AI agent, including the tools it normally uses, resources it accesses, actions it performs, and typical execution patterns. Establishing this baseline helps security teams distinguish legitimate activity from potentially malicious behavior. Deviations such as accessing a new data source, invoking an unusual API, or executing unexpected commands can indicate compromise or configuration drift.

Baselines should be reviewed and updated as agent workflows, models, tools, and permissions change. Security teams should investigate significant deviations rather than automatically treating new behavior as trusted. Combining baseline monitoring with periodic permission reviews and policy testing helps identify excessive access, unexpected capabilities, and emerging attack paths before they lead to a security incident.

Key actions:

Establish expected tools, resources, actions, and execution patterns for each agent.
Alert on significant deviations from normal agent behavior.
Reassess baselines after changes to models, tools, workflows, or permissions.
Investigate anomalous behavior before adding it to the trusted baseline.

11. Constrain Autonomy and Limit Functionality

AI agents should have only the tools, extensions, and capabilities required for their assigned tasks. Removing unnecessary functionality reduces the attack surface without taking away capabilities the workflow actually needs. For example, an agent that only summarizes information should not also be able to send messages.

Autonomy should also be limited according to the risk of each action. High-impact operations can require human approval, while external controls can restrict which data, applications, and destinations an agent can access. These boundaries remain effective even when the model makes an unsafe decision. The goal is to match the agent’s capabilities and autonomy to its purpose while preserving the functions required for productive work.

Key actions:

Limit tools and extensions to those required for the task.
Apply least privilege to each agent and action.
Require human approval for high-impact operations.
Restrict when and how agents can act autonomously.
Enforce data, application, and communication boundaries outside the model.

Governing AI Agent Behavior at the Endpoint with Airlock Digital

Most AI agent security best practices depend on being able to see and control what an agent actually does once it starts running. Airlock Digital Agentic AI Usage Controlextends application control to the endpoint layer, where AI agents execute commands, interact with applications, and perform work. Rather than relying only on the controls built into AI platforms, organizations get independent visibility into trusted AI agent behavior, centralized policy management, and real-time enforcement of what those agents are allowed to do.

Key capabilities of Airlock Digital Agentic AI Usage Control:

Agent discovery across endpoints: Automatically discovers Agentic AI applications and AI agents across the environment, showing what is installed, where it is running, and how it is being used, so unknown agents don't become blind spots.
Trust boundary definition: Centrally sets what trusted AI agents are permitted to do, including the commands they can execute, the files they can access, and the resources they can interact with.
Real-time command governance: Evaluates AI agent commands against policy as they happen and communicates policy decisions back to the agent, steering it so it adjusts its behavior.
Unified application and AI policy management: Manages policies for trusted applications and trusted AI agents in one place, with version-controlled policy changes and granular control over who can modify policy.
Session-level monitoring and evidence: Monitors and searches AI agent sessions, commands, files, policy decisions, risk activity, credential exposure, token usage, and estimated cost from a centralized dashboard.
Extension of native AI controls: Complements platform-native governance with independent endpoint enforcement across supported AI platforms including Microsoft Copilot, Claude, and Codex.

Learn more about Airlock Digital Agentic AI Usage Control and see what your AI agents are really doing on your endpoints.

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