Best practices for AI agent security in 2025

AI agents have evolved from simple chatbots to sophisticated systems capable of orchestrating complex workflows, accessing production databases, and making autonomous decisions across enterprise environments. This transformation brings unprecedented productivity gains but also introduces security challenges that traditional application defenses weren't designed to handle.

The fundamental security weakness of AI agents lies in their inability to reliably distinguish between instructions and data, creating vulnerabilities that attackers can exploit through prompt injection, context poisoning, and other novel attack vectors. Unlike conventional software that follows predetermined logic, AI agents interpret goals and take initiative, potentially touching dozens of APIs, systems, or databases in ways that developers never anticipated.

Organizations deploying AI agents with production data access face a critical balancing act: maximizing the agents' capabilities while maintaining strict security controls to protect sensitive information, ensure compliance, and prevent both malicious attacks and well-meaning agents from causing unintended harm. This requires a comprehensive security framework built specifically for the unique challenges of autonomous AI systems.

What is AI agent security?

AI agent security encompasses the strategies, technologies, and practices designed to protect autonomous AI systems that interact with production data and critical business infrastructure. At its core, this discipline addresses the unique risks that emerge when AI agents operate with varying degrees of autonomy — from simple task automation to complex decision-making across multiple systems and data sources.

The security challenge stems from several fundamental characteristics of AI agents. First, these systems process both structured commands and unstructured data through the same neural pathways, making them vulnerable to what security researchers call the "Lethal Trifecta": the dangerous combination of access to sensitive data, exposure to untrusted content, and the ability to communicate externally. When an agent possesses all three capabilities simultaneously, it becomes a prime target for sophisticated attacks like prompt injection, where malicious instructions hidden in seemingly innocent data can trick the agent into revealing confidential information or performing unauthorized actions. Research shows that 56% of prompt injection tests against 36 large language models resulted in successful exploitation, highlighting that prompt injection is one of the most widespread and practical attacks against AI agents currently deployed.

The stakes are particularly high in enterprise environments where AI agents increasingly handle customer data, financial transactions, and critical infrastructure operations. A compromised agent with elevated privileges can trigger cascading failures across interconnected systems, while even well-intentioned agents operating outside their intended parameters can cause significant disruption. Compounding the challenge, 68% of corporate executives report violating their own AI usage policies within a three-month period, and 82% of executives believe their AI tools meet security requirements even while flouting their own corporate rules. This reality demands a proactive security posture that anticipates both external threats and internal risks, building safeguards into every layer of the AI agent lifecycle from development through deployment and ongoing operation.

Securing AI agents involves implementing advanced authentication strategies to confirm their identities. Machine-to-machine (M2M) authentication using cryptographic algorithms strengthens security by providing agents with distinct, traceable credentials. Leveraging protocols like OAuth 2.0 ensures seamless authentication processes without requiring human intervention. Recent findings indicate that 80% of organizations show detectable signs of shadow AI activity across business functions, with 70-80% of this traffic evading traditional network monitoring tools, and nearly 10% of employees admitting to bypassing corporate AI restrictions to continue using external tools. Regular updates and management of these credentials are crucial to preventing unauthorized access, forming a foundational aspect of robust security measures.

Modern AI agent security requires a multi-layered defense strategy that goes beyond traditional perimeter security. The first documented large-scale cyberattack executed by agentic AI occurred in September 2025, where AI systems performed 80-90% of the attack work with minimal human intervention. The AI made thousands of requests per second and successfully targeted approximately 30 global organizations including tech companies and government agencies. This includes:

• Authentication and identity verification: Ensuring each AI agent has a unique, verifiable identity with credentials that can be tracked, rotated, and revoked
• Granular authorization controls: Implementing fine-grained permissions that restrict agents to only the resources and actions necessary for their specific tasks
• Behavioral monitoring and anomaly detection: Continuously tracking agent activities to identify deviations from expected patterns that might indicate compromise or malfunction
• Isolation and sandboxing: Containing agents within controlled environments to limit the potential damage from security breaches or unexpected behaviors

The stakes are particularly high in enterprise environments where AI agents increasingly handle customer data, financial transactions, and critical infrastructure operations. A compromised agent with elevated privileges can trigger cascading failures across interconnected systems, while even well-intentioned agents operating outside their intended parameters can cause significant disruption. This reality demands a proactive security posture that anticipates both external threats and internal risks, building safeguards into every layer of the AI agent lifecycle from development through deployment and ongoing operation.

How to ensure AI agents that have access to production data are secure?

Securing AI agents involves implementing advanced authentication strategies to confirm their identities. Machine-to-machine (M2M) authentication using cryptographic algorithms strengthens security by providing agents with distinct, traceable credentials. Leveraging protocols like OAuth 2.0 ensures seamless authentication processes without requiring human intervention. Regular updates and management of these credentials are crucial to preventing unauthorized access, forming a foundational aspect of robust security measures.

Incorporating precise access control mechanisms is essential for maintaining data integrity. Research indicates that 97% of AI-related security breaches involved systems that lacked proper access controls, and organizations with shadow AI activity experienced an additional $670,000 in breach costs compared to those with minimal shadow AI use. By defining specific roles and permissions aligned with each agent's tasks, organizations can minimize security vulnerabilities. Contextual authorization enhances this by dynamically adjusting permissions based on real-time task requirements and environmental factors. These measures ensure agents access only the necessary data, mitigating the risk of accidental exposure or misuse.

Implementing containment strategies through isolation techniques is critical for safeguarding AI agents. Utilizing secure environments ensures agents operate within their designated boundaries, reducing potential security breaches. Continuous oversight through monitoring systems allows for the detection of anomalies and rapid response to any irregular activities, ensuring agents adhere to established security protocols.

Step 1: strengthen AI agent authentication

Securing AI agents requires implementing advanced identity verification methods. Leveraging cryptographic techniques for machine-to-machine (M2M) interactions ensures each AI agent operates with a distinct set of credentials. This approach integrates seamlessly with enterprise security frameworks, enhancing system integrity without compromising efficiency.

Regularly updating and managing access credentials fortifies security. By frequently refreshing access keys, organizations minimize the risk of unauthorized intrusions, ensuring that only verified agents interact with production data. This proactive strategy aligns with industry best practices, safeguarding sensitive information.

Incorporating additional security measures like mutual TLS can provide an extra layer of protection. By using certificates to validate both agents and servers, secure communication channels are established. These comprehensive authentication strategies enable enterprises to confidently deploy AI agents, maintaining robust protection for critical data.

Step 2: implement granular access controls

Establishing detailed access controls is crucial for maintaining a secure environment for AI agents. By defining specific roles and permissions, organizations can ensure that each agent functions exclusively within its intended parameters. This precise allocation of access rights not only protects sensitive data but also optimizes workflow efficiency by granting agents only the necessary privileges for their tasks.

Adaptive authorization further enhances security by modifying permissions in response to changing conditions such as the nature of the task or the sensitivity of the data involved. This responsive approach ensures that agents align with organizational policies and compliance requirements, providing robust protection against potential threats. Integrating these adaptive controls into the security framework ensures agents access only what is pertinent to their operations, reducing exposure to risks.

Incorporating mechanisms that restrict access to sensitive content ensures that only authorized users and agents interact with critical data. This strategy prevents unauthorized data exposure and modifications, reinforcing the security of the enterprise environment. By embedding these comprehensive access controls into existing workflows, organizations can maintain productivity while safeguarding vital assets.

Step 3: leverage agent isolation and monitoring

Maintaining awareness of emerging AI security challenges is crucial for adapting defenses. Engaging with industry research and security forums keeps organizations informed about potential risks and innovative protection strategies. For example, recent research shows that as few as 250 poisoned documents are sufficient to successfully backdoor large language models ranging from 600 million to 13 billion parameters, and that poisoning effectiveness depends on absolute count rather than relative proportion, making large models more vulnerable than previously believed. This ongoing education enables teams to anticipate changes in the threat landscape and adjust security measures accordingly.

Vigilant oversight is crucial for sustaining AI agent security. Implementing sophisticated behavior analysis tools allows organizations to detect and address unusual patterns swiftly. This proactive stance ensures rapid intervention in case of irregular activities, thus safeguarding operational integrity and minimizing risk.

Incorporating comprehensive monitoring capabilities within the security infrastructure guarantees that agents function within pre-established limits. These systems offer real-time visibility into agent operations, enabling prompt responses to unauthorized actions and ensuring adherence to security policies. This robust approach ensures AI agents remain secure and efficient, protecting essential business resources.

Tips on AI agent security

1. Implement continuous evaluation of AI agent activities for compliance.

Adopt a continuous evaluation approach to monitor AI agent activities, ensuring they adhere to security protocols. By employing real-time tracking and analysis tools, organizations can quickly identify deviations from expected behavior and respond promptly to mitigate risks. This proactive stance minimizes the likelihood of unauthorized actions and reinforces a secure operational environment.

Integrating advanced monitoring solutions enhances the ability to detect anomalies and potential security breaches. These tools provide comprehensive insights into agent interactions, helping maintain alignment with internal policies and industry standards. Regular updates to monitoring systems ensure they remain effective against evolving threats, supporting a dynamic security strategy.

2. Enhance awareness of emerging AI security challenges and update defenses.

Maintaining awareness of emerging AI security challenges is crucial for adapting defenses. Engaging with industry research and security forums keeps organizations informed about potential risks and innovative protection strategies. This ongoing education enables teams to anticipate changes in the threat landscape and adjust security measures accordingly.

Incorporating the latest security technologies and methodologies, such as predictive threat modeling, equips enterprises to preemptively counteract new vulnerabilities. Encouraging collaboration and information sharing among security professionals fosters a culture of vigilance and adaptability, ensuring robust protection for AI systems.

As AI agents become increasingly central to enterprise operations, implementing these security best practices isn't just about risk mitigation — it's about unlocking the full potential of AI while maintaining the trust and safety your organization demands. The right security framework enables you to deploy AI agents confidently, knowing that your production data remains protected while your teams gain unprecedented productivity and insights.

We understand that securing AI agents requires both technical expertise and a platform built with enterprise security at its core. Request a demo to explore how Glean and AI can transform your workplace — our team can show you how modern AI platforms implement these security best practices while delivering the productivity gains your organization needs.