The rapid evolution of enterprise technologies has introduced AI agents: autonomous systems designed to independently make decisions and execute tasks, into core business operations. These agents are now integral to functions like customer service, data processing, and automation, prompting fresh identity security and governance concerns.
AI agents are increasingly capable, but their autonomy, speed, and potential for large-scale action bring unique risks. Conventional identity security strategies focused on humans or static machines can fall short when applied to these learning, adaptive systems.
This introductory guide examines the distinctive attributes of AI agent identities, the security risks they pose, and best practices for governing their lifecycle, access, and accountability. It builds on decades of expertise in human identity governance, representing the next step in extending that foundation to a new class of identities: AI agents.
What are AI agent identities?
An AI agent identity is an autonomous software entity empowered to interact with data and systems, making independent decisions toward defined goals. Unlike static machine identities, AI agents can learn, adapt, and execute unsupervised tasks—sometimes creating and managing other digital identities in the process.
Key differences among identity types:
- Human identities are linked to employees or contractors and governed by structured processes inherent to human resources and organizational roles.
- Machine identities include service accounts, certificates, and application programming interfaces (APIs). These identities lack consistent governance, often persisting without regular oversight.
- AI agent identities extend beyond other machine identities by learning and acting autonomously, operating with access to a wide range of systems and data sources, frequently at high velocity and scale.
Legacy identity and access management (IAM) frameworks are typically not designed to accommodate identities that act with such autonomy and frequency.
Security risks posed by AI agents
AI agents present a distinct set of risks, particularly when not governed by comprehensive identity security controls.
Data exposure and excessive permissions
AI agents regularly process sensitive or regulated information. Without clear access controls, agents may be granted broad permissions, increasing the risk of data leaks or unauthorized access. The scale and speed of AI agents' interactions complicate traditional static control mechanisms.
Governance and accountability gaps
Many organizations lack formal processes to track, assign ownership to, or review AI agent identities. Issues can arise from:
- Shadow AI: Agents created and deployed outside IT governance, bypassing central review.
- Unclear ownership: Unassigned or shared responsibility, hindering oversight and swift incident response.
- Lifecycle mismanagement: Failure to deprovision agents or their credentials after they are retired.
A Dimensional Research report highlights that more than half of organizations acknowledge gaps in visibility and ownership over AI agent access and actions.
Autonomous and unintended actions
With the capability to operate independently, AI agents may misinterpret instructions or act unpredictably, especially if not adequately monitored. This can result in the misuse of privileged access, propagation of errors, or inappropriate data handling.
Manipulation and prompt injection
AI agents are susceptible to manipulation, such as prompt injection. Threat actors can exploit poorly governed agents to escalate privileges or trigger unintended actions within enterprise systems.
Compliance and regulatory exposure
Uncontrolled AI agent activity can threaten compliance with standards like General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), or Sarbanes-Oxley Act (SOX), exposing organizations to penalties, reputational damage, or audit failures.
Core principles of AI agent security
Effective AI agent governance is grounded in identity-centric practices, adapted for the autonomy and complexity these agents introduce.
1. Identity-centric governance
AI agents must be discoverable, registered, and treated as distinct digital identities:
- Systematically inventory all agents and register them within a central repository.
- Assign unique identities and contextual attributes to each agent based on specific business purposes.
This approach enables comprehensive oversight and policy enforcement.
2. Least privilege access
Grant AI agents only the access they strictly require. Practices include:
- Defining and enforcing dynamic access policies.
- Applying attribute-based controls factoring in agent roles and data sensitivity.
- Issuing short-lived credentials to minimize standing privileges.
Reducing unnecessary access lowers risk in case an agent is compromised.
3. Clear ownership and accountability
Assign ownership, preferably to individuals or well-defined teams, for each AI agent identity. Owners are responsible for ongoing oversight, access reviews, and decommissioning when end of life is reached.
Defined ownership ensures incidents are addressed effectively.
4. Continuous monitoring and visibility
AI agents require real-time monitoring and logging to detect abnormal or unauthorized activities:
- Implement identity-aware telemetry to build an auditable record of agent behavior.
- Apply behavioral analytics to identify deviations from expected patterns.
Continuous visibility is key to early threat detection and supports compliance efforts.
5. Comprehensive lifecycle management
Govern each agent’s entire lifecycle:
- Provision agents for specific, approved purposes.
- Conduct periodic access reviews and recertify or revoke permissions as business needs change.
- Ensure agents and credentials are deprovisioned upon retirement.
A Practical Best Practices Guide for Agentic AI recommends incorporating robust lifecycle management policies as a baseline for organizational governance.
Best practices for managing AI agent identities
Organizations can reduce risk and support compliance by integrating best practices tailored for agentic digital identities.
Inventory and classification
Perform a comprehensive inventory to identify all AI agents, including those deployed outside traditional IT channels. Classify them by:
- Business function
- Data sensitivity
- Environment accessed
- Risk exposure
Centralized visibility supports oversight and informed policy decisions.
Request and approval workflows
Implement structured processes for agent provisioning, including documenting the use case and permissions required, assigning an accountable owner, and specifying decommissioning criteria. Formal workflows help maintain alignment with governance objectives.
Regular access reviews
Schedule regular recertification cycles to review agent permissions, ensure alignment with business needs, and revoke unnecessary access. Special attention should be given to indirect access paths that may allow unintentional exposure.
Governance for associated service accounts
Document, control, and routinely rotate credentials and service accounts used by AI agents. Decommission these promptly alongside agent retirement to limit orphaned or over-permissioned identities.
Behavioral and activity monitoring
Deploy monitoring that tracks agent actions and flags anomalies, such as unusual access patterns, privilege escalation attempts, or large data transfers. Early detection mechanisms help contain threats.
Comprehensive audit trails
Maintain detailed records of agent activity, including access events, resource usage, ownership, and policy compliance. Well-maintained logs are foundational to incident response and auditing.
Tools for agent identity security
Given their complexity, AI agents may require technology solutions beyond traditional IAM systems. Effective agent identity security tools enable:
- Aggregation of agent identities across diverse cloud and on-premises environments.
- Automated ownership assignment and robust context-enrichment for each agent identity.
- Workflows for access certification and automated recertification.
- Policy enforcement spanning human, machine, and AI agent identities within one platform.
Use cases across industries
Organizations in varied sectors use identity-centric approaches to govern AI agents:
- Healthcare: Ensuring AI agents handling patient records meet HIPAA and data privacy requirements.
- Financial services: Applying stringent controls to agents conducting financial analyses and transaction processing under regulatory mandates.
- Manufacturing: Monitoring and managing supply chain AI agents to protect intellectual property and maintain partner trust.
- Utilities: Governing AI-driven grid management systems to ensure compliance with energy regulations and prevent disruptions.
- Oil and Gas: Securing AI agents that control critical infrastructure and ensuring compliance with environmental and safety regulations.
- Government: Governing AI agents used in citizen services and defense systems to safeguard sensitive data, maintain transparency, and meet strict compliance mandates.
- Higher Education: Managing AI agents supporting research, admissions, and student data to ensure academic integrity, data protection, and responsible AI use.
These sector-specific applications underscore the importance of flexible, policy-driven governance frameworks.
Next steps
AI agents have become integral to enterprise operations, making their governance a critical and non-optional priority. Proactive steps organizations should take:
- Inventory AI agents to establish visibility.
- Assess and strengthen access controls for agent identities.
- Define clear ownership and accountability structures.
- Implement ongoing monitoring and logging tailored to agent activity.
- Plan for future growth by embedding governance into enterprise technology strategies.
For further resources and expert guidance, visit
sailpoint.com/products/agent-identity-security
DISCLAIMER: THE INFORMATION CONTAINED IN THIS DOCUMENT IS FOR INFORMATIONAL PURPOSES ONLY, AND NOTHING CONVEYED IN THIS DOCUMENT IS INTENDED TO CONSTITUTE ANY FORM OF LEGAL ADVICE. SAILPOINT CANNOT GIVE SUCH ADVICE AND RECOMMENDS THAT YOU CONTACT LEGAL COUNSEL REGARDING APPLICABLE LEGAL ISSUES
