What Is AI Security?
AI security is the practice of protecting artificial intelligence systems from attacks, theft, and misuse while also using machine learning to strengthen overall cybersecurity. There are two distinct angles to AI security:
First, it covers protecting AI systems themselves, including their models, data, infrastructure, and workflows, from unauthorized access, manipulation, data leakage, and other attacks.
Second, AI security includes using AI to strengthen security controls. In identity and access management, AI can analyze access patterns, identify risky permissions or unusual behavior, and help govern who can access sensitive systems and data. This makes AI both an asset that organizations need to secure and a tool for managing access and identity risk.
Why Is AI Security Important?
Rapid Enterprise Adoption of Generative AI
Organizations are integrating generative AI into software development, customer support, document processing, search, analytics, and other workflows. Employees can also adopt public AI services independently, sometimes without security review. This makes it difficult for security teams to maintain an accurate inventory of:
Models
Applications
Integrations
The data they process
Rapid adoption can create uncontrolled data flows between enterprise systems and external AI providers. Sensitive source code, customer records, credentials, or internal documents might be included in prompts or retrieval pipelines. Organizations need policies and technical controls that determine which AI services are approved, what data they can process, and how AI activity is monitored.
Generative AI applications also add new components to the application stack. Vector databases, model APIs, plugins, retrieval systems, and orchestration frameworks can each introduce vulnerabilities or configuration errors. Securing the model alone is therefore insufficient; teams must protect the complete path between users, models, data, and connected systems.
Growing Number of Non-Human Identities
AI applications and agents use service accounts, API keys, access tokens, workload identities, and other machine credentials to interact with enterprise resources. As organizations deploy more AI workloads, the number of these non-human identities can increase rapidly. Each identity represents another set of credentials and permissions that security teams must manage.
These identities are particularly risky when they receive broad permissions or use long-lived credentials. An agent might need access to email, cloud storage, databases, and third-party APIs to complete a workflow. If its identity is compromised, an attacker may inherit those permissions and use the agent as a route into multiple systems.
Organizations need to inventory non-human identities and apply least-privilege access based on each workload's actual requirements. Factors that can reduce exposure include:
Short-lived credentials
Regular permission reviews
Secret rotation
Centralized monitoring
Security teams should also be able to associate an AI action with the identity, application, and user that initiated it.
Increasing Autonomy of AI Agents
AI agents differ from basic chatbots because they can perform actions rather than only generate responses. An agent may:
Plan a task
Select tools
Query databases
Modify records
Execute code
Communicate with external services
These capabilities increase the consequences of incorrect or malicious model behavior. For example, prompt injection in a document or web page could instruct an agent to ignore its intended task and perform an unauthorized action. An agent can also make incorrect decisions because of ambiguous instructions or unreliable model output. When several actions are chained together, a small error early in the process can affect later steps.
Security controls should limit the tools, data, and actions available to each agent. High-impact operations may require explicit user approval or additional authorization checks. Logging tool calls, inputs, outputs, and permission decisions also gives security teams the information needed to investigate unexpected agent behavior.
Regulatory and Compliance Requirements
AI systems often process financial records, personal data, health information, payment data, and other regulated information. As a result, existing security and privacy frameworks still apply. Depending on the organization and data involved, this can include:
SOX
SOC 2
ISO 27001
HIPAA
PCI-DSS
NYDFS cybersecurity requirements
NIS2
GDPR
These frameworks establish requirements for areas such as access control, risk management, auditability, data protection, incident response, and security governance. AI also introduces risks that traditional security frameworks do not fully address. Emerging AI-specific regulations and standards, including the EU AI Act, ISO/IEC 42001, and the NIST AI Risk Management Framework, focus more directly on AI governance, model risk, transparency, human oversight, and lifecycle controls.
Identity and access controls are relevant to both groups of requirements. Organizations need to know which users, service accounts, agents, and workloads can access AI systems and sensitive data, whether those permissions are appropriate, and how access decisions are recorded. Strong identity governance can help support compliance evidence while also reducing the security risks created by AI adoption.
What Are the Main AI Security Risks?
1. Prompt Injection
Prompt injection occurs when malicious instructions cause an AI system to ignore or override its intended behavior. Attackers can place these instructions directly in a prompt or indirectly in content the model processes, such as documents, emails, or web pages. The risk becomes more serious when the model is connected to tools or sensitive data. A successful injection could cause an agent to disclose information, invoke a tool, or perform an unauthorized action.
How to address:
Controls include isolating untrusted content, restricting tool permissions, validating actions, and requiring approval for sensitive operations.
2. Sensitive Data Leakage
AI systems can expose sensitive information through prompts, generated responses, logs, training data, or retrieval systems. Users may unintentionally submit credentials, source code, customer records, or confidential documents to models that are not approved to process that information.
How to address:
Applications should classify and restrict sensitive data before sending it to AI services. Access controls, data loss prevention, output filtering, encryption, and clear retention policies can further reduce exposure.
3. Data Poisoning
Data poisoning involves manipulating information used to train, fine-tune, or provide context to an AI model. Malicious or inaccurate data can influence model behavior, introduce biases, create backdoors, or cause the system to produce incorrect results. Poisoning can also affect retrieval-augmented generation systems when attackers modify documents in a trusted knowledge source.
How to address:
Organizations should control who can modify source data, track its provenance, validate new datasets, and monitor important sources for unexpected changes.
4. Model Theft and Extraction
Model theft occurs when an attacker obtains model files, parameters, proprietary prompts, or other intellectual property. Model extraction can also occur through repeated queries designed to approximate the behavior of a model without directly accessing its parameters.
How to address:
Strong authentication, authorization, encryption, network controls, and secure storage can protect model artifacts. API rate limits and monitoring can help identify unusual query patterns associated with extraction attempts.
5. Adversarial Attacks
Adversarial attacks use specially constructed inputs to cause a model to make incorrect predictions or behave unexpectedly. Small changes that appear insignificant to a person can sometimes produce substantial changes in model output. These attacks are especially relevant when AI supports security-sensitive decisions such as malware detection, fraud detection, or image recognition.
How to address:
Testing models against adversarial inputs, validating critical outputs, and using additional controls around high-impact decisions can reduce the risk.
6. Insecure AI APIs and Integrations
AI applications often depend on model APIs, databases, plugins, orchestration frameworks, and third-party services. Weak authentication, exposed API keys, vulnerable dependencies, or poorly validated inputs can give attackers access to models and connected resources.
How to address:
Organizations should treat AI integrations like other production interfaces. Credentials should be stored securely, permissions should be narrowly scoped, and API activity should be authenticated, validated, rate-limited, and logged.
7. Excessive AI Agent Permissions
AI agents often need permissions across multiple applications, databases, cloud services, and APIs to complete tasks. When these permissions are broader than necessary, an agent can access sensitive data or perform actions outside its intended role. The risk increases as agents accumulate permissions over time or inherit access through service accounts and other non-human identities.
Excessive access can also create segregation of duties (SoD) conflicts and toxic permission combinations. For example, an agent with permission to create a vendor in one system and approve payments in another can bypass controls designed to separate those functions. This is the same risk created by an overprivileged human user, but an AI agent can execute actions at machine speed and across many systems.
How to address:
Organizations should apply least privilege and continuously evaluate agent permissions across systems, not only within individual applications. Access reviews should detect excessive privileges, SoD violations, and toxic combinations before they can be exploited. High-risk permission combinations should be removed, constrained with additional controls, or require human approval for sensitive actions.
8. Unauthorized Tool and Application Access
An AI system can become an alternative path to tools and applications that users would not normally be allowed to access. This can happen when an agent's permissions exceed those of the user requesting an action or when authorization checks are applied only at the AI interface.
How to address:
Applications should verify authorization when resources are accessed or actions are executed, not simply when a user starts an AI session. The system should preserve the requesting user's identity and enforce applicable permissions throughout the workflow.
9. Shadow AI
Shadow AI refers to AI tools, models, or services used without organizational approval or security oversight. Employees may use public chatbots, browser extensions, coding assistants, or other AI applications because they are easy to adopt without involving IT. This can result in sensitive data being processed by unknown providers with unsuitable security or retention policies.
How to address:
Organizations can reduce shadow AI by discovering AI service usage, defining approved alternatives, enforcing data policies, and monitoring connections to unapproved services.
10. Compromised AI Agents
An AI agent can be compromised through stolen credentials, malicious integrations, prompt injection, poisoned context, vulnerable software, or changes to its configuration. Because agents can operate through non-human identities, compromised activity may continue without an attacker maintaining an interactive session. A compromised agent can use its existing permissions to access data, call tools, or perform actions that appear legitimate.
How to address:
Organizations need behavioral monitoring, credential protection, least-privilege permissions, and detailed audit logs to identify unusual agent activity and contain compromised identities quickly.
How Does AI Security Work?
AI Asset and Agent Discovery
The first step is identifying the AI assets operating across the environment. This inventory can include:
Models
AI applications
Agents
Datasets
Model endpoints
Vector databases
Plugins
Supporting infrastructure
Discovery also helps identify unapproved or unmanaged AI services. Security teams need to know who owns each asset, where it runs, what data it processes, and which systems it connects to before they can apply appropriate controls.
Identity and Permission Mapping
Identity and permission mapping connects AI agents and other non-human identities to the resources they can access. This includes:
Direct permissions
Inherited roles
Group memberships
Service accounts
API credentials
Cross-system entitlements
Mapping these relationships helps identify excessive access, privilege escalation paths, SoD conflicts, and toxic permission combinations. Permission data alone, however, only shows what access exists. It does not show whether that access is justified. Usage telemetry, including last-used timestamps and access frequency, is needed to distinguish actively required permissions from dormant or unnecessary access.
Risk Assessment and Prioritization
Risk assessment evaluates AI assets based on factors such as:
Data sensitivity
External exposure
Vulnerabilities
Permissions
Agent capabilities
It can also consider whether an AI system can execute code, invoke tools, or make changes without human approval. These factors help security teams prioritize findings according to potential impact rather than treating every issue equally. An exposed agent with access to customer data and administrative tools, for example, generally requires faster remediation than an isolated experimental model with no sensitive access.
Policy Enforcement
Policies define which models, data sources, tools, and actions AI systems are allowed to use. Controls can:
Prevent sensitive data from being submitted to unapproved models
Block prohibited tools
Restrict agent permissions
Require approval before high-impact actions
Policy enforcement should occur at relevant control points throughout the workflow. This includes authentication, data retrieval, model invocation, and tool execution. Enforcing authorization close to the protected resource also prevents the AI layer from becoming a way to bypass existing access controls.
Runtime Monitoring
Runtime monitoring observes AI systems while they process requests and perform actions. It can capture:
Prompts
Model responses
Tool calls
Data access
Authentication events
Changes made by AI agents
This visibility is particularly important for autonomous agents because a single request can trigger multiple downstream operations. Runtime records allow security teams to reconstruct what an agent did, which resources it accessed, and which identity authorized each action.
Threat and Anomaly Detection
Threat detection should analyze how AI agents actually use their identities, entitlements, and tools. Peer-group analytics can compare agents with similar roles or functions and identify outliers, such as an agent:
Accessing unusual datasets
Invoking privileged tools
Operating at unexpected times or volumes
Anomaly detection can also identify changes in tool usage patterns, access frequency, and resource interactions that may indicate compromise or unintended behavior. AI agent entitlements can be automatically classified by risk based on factors such as privilege level, data sensitivity, tool capabilities, and toxic combinations. This gives security teams unique identity and access risks to investigate rather than relying on generic AI-assisted detection.
Automated Risk Remediation
Automated remediation reduces risky access by removing unused permissions, revoking excessive entitlements, rotating credentials, disabling compromised identities, or requiring approval for sensitive actions. To avoid disrupting legitimate agent workflows, remediation decisions should account for:
Entitlement risk
Actual usage
Resource sensitivity
Dependencies
Effective remediation requires a closed-loop workflow that connects detection directly to revocation. Identifying excessive or anomalous access without a reliable way to remove it leaves the underlying exposure in place. The gap between detecting a risk and actually revoking access is where many tools fall short. Automation can close that gap by moving validated findings through approval, revocation, and verification in a single workflow.
Continuous Governance and Auditing
AI environments change as organizations deploy new models, connect additional data sources, and give agents new capabilities. Continuous governance tracks these changes and checks whether AI systems continue to comply with security, privacy, and organizational policies. Auditing provides records of:
AI assets
Permissions
Policy decisions
Data access
Agent actions
These records support security investigations and compliance reviews while helping teams identify control gaps. Regular reassessment is necessary because an AI system that was low risk at deployment can become higher risk as its access and capabilities expand.
AI Security Frameworks and Standards
NIST AI Risk Management Framework
The NIST AI Risk Management Framework (AI RMF) is a voluntary framework for managing risks associated with designing, developing, deploying, and using AI systems. Its core functions are Govern, Map, Measure, and Manage. These functions provide a repeatable process for incorporating AI risk management into organizational practices:
Govern: Establish policies, responsibilities, accountability structures, and processes for AI risk management. Governance should cover the entire AI lifecycle and define who makes decisions about acceptable risk.
Map: Identify the purpose, operating environment, users, affected parties, data, dependencies, and potential impacts of an AI system. Mapping provides the context needed to evaluate risk accurately.
Measure: Assess identified risks using appropriate metrics, testing, evaluation, and monitoring. This can include security, privacy, reliability, explainability, fairness, and robustness testing.
Manage: Prioritize identified risks and determine how they should be mitigated, accepted, transferred, or avoided. Controls should be monitored to verify that they continue to work as intended.
Continuous risk management: Reassess AI systems as models, data, integrations, users, and operating conditions change. Risks identified before deployment may change substantially once a system operates in production.
NIST Guidance for AI and Adversarial Machine Learning
NIST provides additional guidance on adversarial machine learning to help organizations understand attacks against AI systems. Its adversarial machine learning taxonomy describes attacker goals, capabilities, and techniques across different types of machine learning systems, including generative AI:
Evasion attacks: Manipulate inputs during inference so a model produces an incorrect result. An attacker may make small or carefully selected changes designed to bypass an AI-based classifier.
Poisoning attacks: Modify training or other learning data to influence model behavior. Poisoning can reduce general model accuracy or create targeted behavior that appears only under specific conditions.
Privacy attacks: Attempt to recover sensitive information about training data or model inputs. Examples include membership inference and techniques designed to reconstruct information from a model.
Abuse attacks: Use generative AI capabilities for purposes that conflict with their intended operation. Security assessments should consider both attacks on an AI system and misuse of capabilities provided by the system.
Threat modeling: Evaluate attacker knowledge, access, objectives, and capabilities rather than treating all adversarial attacks as equivalent. This helps teams select controls that address realistic attack scenarios.
OWASP Top 10 for LLM Applications
The OWASP Top 10 for LLM Applications, maintained as part of OWASP's work on generative AI security, identifies major vulnerability classes affecting applications built with large language models. It gives developers and security teams a practical basis for threat modeling, architecture reviews, testing, and control selection:
Prompt-related attacks: Untrusted instructions can manipulate model behavior and potentially influence connected tools or data access. Both direct user prompts and indirect instructions embedded in external content need to be considered.
Sensitive information disclosure: Models and applications can expose confidential information through responses, context windows, retrieval systems, or poorly controlled access to enterprise data.
Supply chain risks: Models, datasets, libraries, adapters, and third-party services can introduce malicious code, vulnerabilities, or compromised components into an AI application.
Data and model poisoning: Manipulated training, fine-tuning, or knowledge-base content can influence model behavior and create persistent weaknesses.
Excessive agency: Agents with unnecessary tools or broad permissions can turn an incorrect or manipulated model decision into a high-impact action.
Application controls: Authentication, authorization, input handling, output validation, rate limiting, least privilege, and monitoring remain important even when an LLM sits at the center of the application.
MITRE ATLAS
MITRE ATLAS is a knowledge base describing adversarial tactics and techniques targeting AI-enabled systems. It provides a common language for documenting attacker behavior and can be used alongside MITRE ATT&CK when an incident involves both conventional infrastructure and AI components:
Tactics and techniques: ATLAS organizes attacker behavior into objectives and the techniques used to achieve them. This helps teams break an AI attack into discrete stages rather than treating it as a single event.
Real-world procedures: Documented case studies and procedures show how techniques can appear in actual attacks or security research. These examples can help teams develop realistic threat scenarios.
Threat modeling: Architects can map techniques to models, datasets, APIs, pipelines, and other AI assets to identify likely attack paths before deployment.
Red teaming: Security teams can use ATLAS techniques to design exercises that test whether models and supporting systems withstand realistic adversarial behavior.
Detection and response: Defenders can map telemetry and security controls to relevant techniques. This helps identify monitoring gaps and provides a structured basis for incident investigation.
ATT&CK alignment: ATLAS can complement ATT&CK when attackers move between AI systems and conventional enterprise infrastructure, such as stealing credentials before accessing a model endpoint.
ISO/IEC 42001
ISO/IEC 42001 is an international standard specifying requirements for an artificial intelligence management system (AIMS). It provides a formal management framework for organizations that develop, provide, or use AI and can be used as the basis for third-party certification:
AI governance: Establish an AI policy, management responsibilities, objectives, and defined processes for controlling AI-related activities across the organization.
Risk management: Identify and assess risks associated with AI systems and establish measures for treating them. Risk management should account for the organization's role and the context in which AI is used.
AI impact assessment: Evaluate potential consequences of AI systems for individuals, groups, and organizations. Assessments help connect technical design decisions with broader impacts.
Lifecycle controls: Apply management processes throughout acquisition, development, deployment, operation, and retirement rather than evaluating AI only before launch.
Documentation and accountability: Maintain records that show how AI-related decisions, responsibilities, risks, and controls are managed.
Performance evaluation: Monitor, measure, audit, and review the AI management system to determine whether it remains effective.
Continual improvement: Address identified weaknesses and update processes as technology, business requirements, risks, and regulatory expectations change.
EU AI Act
The EU AI Act is a European Union regulation that establishes requirements for AI based largely on the risks associated with particular systems and uses. Obligations vary according to the type of AI system and the organization's role, so providers, deployers, importers, distributors, and other parties can have different responsibilities:
Prohibited AI practices: Certain AI uses considered incompatible with fundamental rights or safety requirements are prohibited, subject to the definitions and exceptions in the regulation.
High-risk AI systems: Systems classified as high risk are subject to requirements covering areas such as risk management, data governance, technical documentation, record keeping, human oversight, accuracy, robustness, and cybersecurity.
Transparency requirements: Certain AI systems must provide users or affected people with specified information. Requirements depend on the system and can include disclosure that a person is interacting with AI or that content is artificially generated or manipulated.
General-purpose AI models: Providers of general-purpose AI models have documentation, information-sharing, and other obligations. Additional requirements apply to general-purpose AI models classified as presenting systemic risk.
Cybersecurity and robustness: Covered systems must achieve levels of resilience appropriate to their risks. For high-risk systems, this includes addressing attempts by unauthorized parties to alter system use, outputs, performance, or security.
Documentation and records: Organizations subject to relevant requirements need evidence describing system design, operation, risk controls, and compliance activities. Logging requirements can also support traceability after deployment.
Lifecycle compliance: Applicable obligations extend beyond initial development. Organizations may need processes for post-market monitoring, incident reporting, corrective actions, and reassessment as systems or their uses change.
The Role of Identity and Access Management in AI Security
Identity and access management (IAM) helps organizations control how AI agents authenticate, what resources they can access, and what actions they can perform. Because agents often operate through non-human identities and can act autonomously, IAM controls are essential for limiting their access and reducing the impact of compromised or misbehaving agents:
Discover and inventory AI identities: Identify service accounts, workload identities, API keys, OAuth clients, tokens, and other credentials used by AI agents and applications. Map each identity to its agent, owner, environment, and business purpose. A current inventory also helps detect unmanaged agents and credentials created outside approved processes.
Assign a unique identity to every AI agent: Give each agent a separate identity instead of sharing service accounts or credentials across multiple workloads. Unique identities allow organizations to assign agent-specific permissions and trace actions back to the workload that performed them. Where possible, use managed workload identities and short-lived credentials instead of static secrets.
Understand what each agent can access: Map the effective permissions of every agent across databases, cloud resources, SaaS applications, APIs, files, and tools. Include direct permissions, inherited roles, group memberships, and delegated access. This reveals indirect access paths that may give an agent more capabilities than its primary role suggests.
Enforce least-privilege access: Give agents only the resources and operations required for their assigned tasks. Restrict both which tools an agent can invoke and what it can do within those tools. Sensitive operations such as deleting data, changing infrastructure, or making financial transactions can require additional authorization or human approval.
Detect SoD violations and toxic combinations: Apply segregation of duties (SoD) controls to AI agents just as they are applied to human identities. Analyze permissions across systems to identify combinations that allow an agent to perform conflicting or high-risk actions, such as creating a vendor and approving its payment. SoD detection is especially important for agents because they can execute chained actions autonomously and at machine speed.
Remove excessive and unused permissions: Compare granted permissions with actual agent activity to identify privileges that are unnecessary or no longer used. Remove obsolete roles, broad administrative permissions, and access inherited from outdated groups. Regular rightsizing reduces the potential impact of credential theft, prompt injection, and agent compromise.
Apply context-aware access policies: Make authorization decisions based on factors such as the requesting user, agent identity, resource sensitivity, requested action, and current risk. An agent acting for a user should not automatically provide access to resources that user cannot access directly. High-risk requests can trigger stronger authentication, additional authorization, or approval requirements.
Manage AI agent identity lifecycles: Apply joiner, mover, and leaver (JML) controls to AI agents rather than managing their identities separately from established IAM processes. Where agent ownership, status, and scope are maintained in an HRIS or connected identity source, lifecycle events should trigger corresponding access changes. If an agent is decommissioned or its scope changes, its credentials, tokens, roles, group memberships, and application access should be revoked or updated automatically, just as they would be when a human employee leaves or changes roles.
Continuously monitor AI identity risk: Monitor authentication events, permission changes, resource access, and agent behavior for signs of compromise or misuse. Look for unusual tool calls, access to previously unused resources, privilege escalation, or abnormal credential activity. High-risk events can trigger actions such as blocking access, revoking credentials, reducing permissions, or isolating the agent.
AI Security Best Practices
Here are some of the ways that organizations can improve their AI security posture.
1. Maintain an Inventory of AI Applications and Agents
Maintain a centralized inventory of AI applications, models, and agents operating across the organization. For each asset, record its owner, business purpose, deployment environment, model provider, data sources, connected applications, tools, and identities. The inventory should include internally developed systems, third-party AI services, embedded AI features, and experimental deployments. An accurate inventory provides the foundation for risk assessments, access reviews, incident response, and compliance audits.
Key actions:
Use automated discovery where possible to detect new or unmanaged AI assets as they appear.
Connect inventory information with configuration, identity, and usage data to identify shadow AI, abandoned agents, unknown integrations, and systems processing sensitive information.
Record each asset's risk classification, data sensitivity, lifecycle status, and responsible owner so unmanaged or high-risk AI systems can be prioritized for review.
2. Apply Least Privilege to AI Agents
Give each AI agent only the permissions required to complete its defined tasks. Scope access to specific resources and operations instead of assigning broad roles. For example, an agent that summarizes documents may require read access to a particular repository but should not receive permission to modify, share, or delete those documents.
Key actions:
Apply the same principle to tools, APIs, databases, and execution capabilities.
Use short-lived credentials and just-in-time elevation instead of persistent privileged access wherever possible.
Regularly compare granted permissions with actual usage and remove unused roles, scopes, and tool capabilities.
An agent should only be able to invoke the tools required for its workflow and only perform approved operations through them. Sensitive actions, such as changing production systems, executing arbitrary code, deleting data, or completing financial transactions, can require additional authorization or explicit human approval.
3. Continuously Review AI Permissions and Entitlements
Agent access can expand over time as teams add new integrations, data sources, and responsibilities. Continuously review direct permissions, inherited roles, group memberships, OAuth scopes, delegated permissions, and application entitlements. Reviews should calculate effective access rather than examining individual permission assignments in isolation.
Key actions:
Compare granted access with actual agent behavior to identify permissions that are unused, excessive, or inconsistent with the agent's purpose.
Prioritize agents that combine powerful capabilities with access to sensitive data or critical infrastructure.
Remove obsolete privileges promptly and investigate unexpected permission changes that could indicate configuration errors or compromise.
4. Apply Zero Trust Principles to AI Access
Do not assume that an authenticated AI agent or application should automatically be trusted. Evaluate each access request using the agent identity, requesting user, target resource, requested action, and relevant risk signals. Authorization should be enforced when the protected resource or tool is accessed, not only when the AI session begins.
Key actions:
Use strong workload authentication, short-lived credentials, least-privilege authorization, and segmentation to reduce persistent trust.
When agents act on behalf of users, preserve both the user and agent identities throughout the workflow.
Reevaluate authorization when an agent changes tools, resources, or actions during a workflow rather than carrying trust forward automatically.
5. Maintain Detailed AI Activity and Access Logs
Record enough information to reconstruct how important AI actions occurred. Logs should capture agent and user identities, authentication events, authorization decisions, data access, API requests, tool calls, permission changes, timestamps, and the results of sensitive operations. For autonomous workflows, preserve the relationship between individual actions so investigators can follow the complete sequence.
Key actions:
Centralize AI logs and correlate them with identity, application, cloud, endpoint, and network telemetry.
Protect the logs themselves with appropriate access and retention controls, especially when prompts, model responses, or retrieved context contain sensitive information.
Configure alerts for unusual patterns such as unexpected tool use, privilege escalation, access to new sensitive resources, or activity outside the agent's normal behavior.
6. Govern the AI Agent Identity Lifecycle
Manage AI agent identities from provisioning through deprovisioning using the same joiner, mover, and leaver (JML) controls applied to human identities. Provision identities through approved workflows, assign an accountable owner, and update permissions when an agent's role, scope, or connected systems change.
Key actions:
When an agent is retired, automatically revoke its credentials, tokens, roles, group memberships, and application access.
Ensure that lifecycle controls detect orphaned identities and inactive agents so access does not remain after the workload that required it has disappeared.
Reassess ownership, permissions, credentials, and connected resources whenever an agent changes purpose, environment, or responsible team.
7. Continuously Detect SoD Violations and Toxic Combinations
Continuously analyze agent permissions for segregation of duties (SoD) violations and toxic combinations across applications and systems. An agent may have individually valid permissions that become dangerous when combined, such as the ability to create a vendor and approve payments. Detection should run as permissions and integrations change, not only during periodic access reviews. Continuous analysis can identify new conflicts as soon as an agent gains an entitlement, inherits a role, or receives access to another system.
Key actions:
Ensure that high-risk combinations trigger remediation, additional controls, or human approval before the agent can use the conflicting permissions.
Evaluate effective permissions across systems rather than checking entitlements within each application independently.
Recheck SoD policies whenever an agent gains a new role, tool, API scope, delegated permission, or application connection.
Why Traditional IAM Tools Fall Short for AI Security
Traditional identity and access management tools were largely designed around human users. Their governance models typically focus on employees, contractors, roles, and application accounts. AI agents introduce non-human identities (NHIs), service accounts, workload identities, API credentials, and delegated permissions that may not fit these models. Without agent-aware discovery and ownership mapping, organizations can miss identities with significant access to sensitive resources.
Periodic access reviews also struggle with the speed of agentic environments. An AI agent can gain new tools, permissions, integrations, or responsibilities between quarterly or annual reviews. Its effective access can therefore change much faster than traditional certification cycles can detect. Continuous monitoring is needed to identify excessive permissions, unused access, SoD violations, and toxic combinations as they emerge.
Static role-based access control (RBAC) presents another limitation. Agent permissions can depend on the task being performed, the user initiating it, the target resource, and the current risk context. Assigning a fixed role to an agent can grant persistent access that is broader than necessary. Agentic environments require more granular and context-aware controls that can constrain individual tools, resources, and actions.
Traditional IAM tools can also leave a gap between visibility and action. Identifying an overprivileged or risky agent does not reduce exposure unless the relevant access is actually removed. Effective AI identity security requires a closed-loop process that connects discovery and risk detection with approval, revocation, and verification. This allows teams to move from finding risky access to removing it before an agent or compromised identity can use it.
Related content: Read our article about the top IAM security risks and the capabilities needed to address them.
Securing AI Agents with Opti
Opti is an AI-native identity security platform focused on governing access across human, non-human, and agentic identities. For AI security programs, it applies identity governance controls to the agents, service accounts, workload identities, and permissions that connect AI systems to enterprise resources.
Opti provides risk visibility and remediation across agentic identities by identifying excessive permissions, unused access, toxic combinations, and other entitlement risks, then helping teams move from detection to remediation. It also supports automated access reviews for AI agents, so organizations can continuously verify whether agent permissions remain appropriate as tools, integrations, owners, and responsibilities change.
Its intelligent access administration capabilities extend provisioning, access changes, approvals, and deprovisioning across human, NHI, and agentic identities. This helps organizations apply least privilege and consistent lifecycle governance to AI agents without treating them as exceptions to established identity processes.
