Agentic Access Management
A Comprehensive Approach to AI Identity and Access Security
Executive Summary
As organizations rapidly adopt AI technologies, a new class of security challenge has emerged: managing the identities and credentials for AI agents. AI agents and services authenticate using service accounts, API tokens, certificates, and other methods—expanding an often overlooked machine-to-machine attack surface that traditional, human-centric IAM solutions were never designed to address.This AI Access Management Framework provides a structured, seven-pillar approach to identifying, managing, and securing all forms of AI-related access throughout its lifecycle. This framework addresses the unique challenges posed by AI systems’ autonomous behavior, resource consumption patterns, and data access requirements while maintaining the operational flexibility required for rapid iteration and innovation.
Organizations implementing this framework can expect to achieve comprehensive visibility into their AI identity landscape, establish accountability and governance structures, implement appropriate technical controls, and maintain continuous security posture improvement - all while enabling safe AI adoption at scale.
Unique Security Challenges
AI-related Non-Human Identities (NHIs) represent a new frontier in cybersecurity. They are often:
- Invisible to traditional identity governance systems
- Long-lived and rarely updated
- Highly privileged with broad access to sensitive resources
- Difficult to monitor due to autonomous, non-deterministic and ephemeral behavior patterns
- Vulnerable to novel attack vectors, including adversarial inputs and model manipulation
These unique properties and attributes bring about specific challenges, making it tougher for organizations to safely, securely, and scalably adopt AI:
Autonomous Behavior at Scale
Unlike deterministic services, AI agents are non-deterministic, e.g. they dynamically choose their tools and access paths, often taking exploratory approaches that involve trial-and-error,, and have higher rates of failed requests compared to human users. At scale, this autonomy and non-determinism make it difficult to distinguish legitimate AI activity from potential security incidents, overwhelming traditional user and entity behavior analytics (UEBA) systems. The challenge is compounded by the fact that many agents are ephemeral, making it difficult to establish stable behavioral baselines.
Resource Exhaustion and Cost Overruns
AI workloads can consume significant computational and financial resources. Compromised AI non-human identities (NHIs) can lead to resource exhaustion attacks, where malicious actors use AI systems to generate excessive costs or consume limited resources for ulterior purposes, impacting business operations.
Data Access and Privacy Implications
Data is the lifeblood of AI, and therefore, AI systems often require access to large amounts of data to function effectively. This broad data access, combined with AI’s ability to process and potentially transmit information externally, creates unique data loss prevention (DLP) challenges that traditional DLP solutions may not adequately address.
Shadow AI and Governance Gaps
The ease of AI service consumption has led to rapid, often ungoverned adoption across organizations. Employees may integrate AI tools and services without proper security review, creating shadow AI deployments with unmanaged identities and unclear data handling practices.
Adversarial Risks and External Influence
AI systems can be manipulated through adversarial inputs designed to alter their behavior or extract sensitive information. When combined with privileged agentic access, these attacks can have far-reaching consequences beyond traditional system compromises.
The Business Impact
The risks associated with inadequate AI identity governance are both diverse and severe, with real-world consequences that can devastate organizations:
Financial Loss
Unauthorized access to foundation models or agents can result in catastrophic financial damage. A single leaked API key for a foundation model can be exploited to generate tens of thousands of dollars in costs per day through excessive consumption. Once rate limits or quotas are exceeded, attackers may trigger service disruptions, degrading availability for legitimate users. Bad actors frequently trade these compromised keys on the dark web, fueling attacks such as LLMJacking. Similarly, they can manipulate compromised agents or tools to perform costly unauthorized actions, rapidly draining organizational resources.
Data Exposure and Privacy Breaches
Sensitive data faces exposure across all AI components. Compromised agents, data sources, or tools can leak confidential customer information, employee data, or proprietary business intelligence. These breaches not only undermine organizational security but also erode stakeholder trust and can trigger cascading security incidents.
Compliance Violations
Unauthorized access to AI agents, data sources, or tools frequently results in violations of critical regulatory requirements such as GDPR, HIPAA, CCPA, and industry-specific standards. These violations can trigger substantial legal penalties, regulatory fines, and loss of essential certifications, fundamentally impacting organizational credibility and market positioning.
Operational Continuity Disruptions
Malicious actors can severely disrupt AI-driven operations through various attack vectors. For foundation models, attackers can exhaust account-level, subscription-level, or zone-level rate limits and quotas, degrading performance and affecting other AI-dependent components. Compromised agents or tools can impair automated workflows, causing significant delays and inefficiencies across the entire organization.
Reputational Damage
Security incidents involving AI systems can have lasting reputationa l consequences. Breaches affecting data sources, tools, or agent behaviors can erode customer confidence and damage hard-earned organizational reputation. Publicized incidents of unauthorized AI access often lead to permanent loss of trust among stakeholders, partners, and customers.
Organizations without proper AI NHI governance face this interconnected web of risks that can compound rapidly, making comprehensive security frameworks not just beneficial but essential for safe AI adoption.
These challenges require a new approach to access security - one specifically designed for the unique characteristics of AI systems and their associated risks.
Access in the AI Ecosystem
Components of the AI Agent Ecosystem
Understanding the interconnected nature of AI systems is crucial for effective NHI security.
Each of the components introduced here can work separately to the rest of the system, can be hosted elsewhere and will need to be accessed in a secure manner.
The following describes the general case for a system:
Access Flow
Each blue edge in the graph represents an access requirement, where the arrow points from the authenticating unit to the resource that requires authentication.
Nodes can authenticate to other nodes in different ways - either by using static, long-lived credentials (e.g., access keys, API keys, client certificates) or by leveraging short-lived, ephemeral credentials (e.g.,. OAuth tokens, STS tokens, JWTs). These credentials, in turn, represent different identity types such as Roles, Service Accounts, Service Principals, Database Users, etc...
In relation to agents, use the terms inbound and outbound access:
Inbound Access refers to the Consumers of the agent, which can be human users, other agents, or standalone software. This flow is typically more controlled, as organizations can implement standard authentication mechanisms and access controls for known consumer types.
Outbound Access refers to resources utilized by agents. Those could be databases, APIs, tools, the internet and even other agents. Agent-to-Agent Authentication creates a complex scenario, as it requires the source agent to authenticate or authorize itself to the target agent, creating additional identity management complexity. These peer-to-peer connections require elevated privileges across diverse technology stacks and create cascading access relationships that are difficult to track and govern.
Each authentication flow generates its own set of NHIs with distinct lifecycle requirements, privilege levels, and security considerations. The challenge lies not just in managing individual identities, but in understanding how they interconnect to create potential attack paths across the entire AI ecosystem.Understanding these access patterns is crucial for implementing appropriate security controls from our seven-pillar framework.
The Agentic Access Management Framework
The framework consists of seven interconnected pillars that provide comprehensive coverage of AI access security concerns. Each pillar builds upon the others to create a robust security posture that evolves with your organization's AI adoption journey.
Establish and maintain a comprehensive inventory of all AI-related non-human identities (NHIs) - for example, roles, service accounts, service principals, and database users. The inventory must document which agents and systems each NHI corresponds to.The inventory must capture both inbound access used to interact with agents, and outbound NHIs used by agents to access external tools, resources, and data.
For each NHI in the inventory, document the authentication methods employed, including authentication type (user access, delegated access, machine-to-machine access), secret storage location and security - when static credentials are used (e.g., secure key vault vs. hardcoded credentials) - OAuth configuration details including discovery endpoints where applicable, and principals who granted consent for delegated access scenarios.
For each NHI recorded in the inventory, identify and document its active usage status and potential, authorized, or known consumers (such as users, systems, or applications). Refer to Section 6.1 for requirements related to analyzing access patterns and frequency of use as part of ongoing monitoring.
Document all resources accessible by AI-related NHIs, including but not limited to: (1) Database access (hosting location, sensitive data presence, access controls granularity, and read/write permissions); (2) SaaS integrations (access levels, service type, trust level and reputation); and (3) Internet access (general vs. restricted access and approved external destinations). See Section 4.2 for management of Data Exfilteration risks.
Continuously identify unauthorized or unmanaged AI tools and services across environments using identity provider signals and endpoint telemetry. Implement automated detection for unapproved AI tools and establish remediation workflows for discovered shadow AI usage.
Ensure every AI-related NHI is assigned to a responsible owner (individual or team) in accordance with organizational governance requirements. Ownership may be assigned directly to human owners or to CMDB items with designated human owners. For instance, each AI agent may have a clearly defined primary owner, and each associated identity may include an additional owner where appropriate, based on the resource type and scope.
Track and retain logs of all humans who manage, or provision static credentials (such as tokens, API keys, certificates) used by AI NHIs or who modify AI NHI configurations and permissions.
Establish processes for ongoing NHI management including regular access reviews and revalidation, automated detection of unused or orphaned identities, and periodic ownership verification. Enable automated decommissioning of unused identities based on contextual signals and customized workflows. See control 3.1 for initial provisioning security requirements and workflow customizations.
Upon employee offboarding or role changes, identify and remediate all AI-related access, including NHI ownership reassignment, credential revocation or rotation, and documentation of access transfer to new owners.
Require AI-related NHIs to be created and initially provisioned only through approved workflows that ensure scoped access based on least privilege principles, vault-backed credential storage, assigned ownership before activation, and documented business justification for the NHI request. Implement automated policy compliance checks during provisioning and enable workflow customization based on geographic, departmental, or risk profile requirements.
Use ephemeral or system-managed credentials where possible to reduce credential leakage risk and simplify lifecycle management.
Store all AI NHI secrets and credentials in approved secure key vaults or secret management systems. Prohibit hardcoded credentials in configuration files or source code.
When long-lived credentials are necessary, enforce regular rotation according to enterprise security policies and support automated rotation where technically feasible. Implement automated detection of credential rotation policy violations and establish escalation procedures for non-compliant credentials.
Detect and flag instances where human-issued credentials (session tokens, browser cookies) are inappropriately used by AI systems or automated processes.
For AI NHIs with access to consumable resources, implement usage controls including rate limiting mechanisms, quota enforcement, cost management boundaries, and LLM access limitations.
For AI NHIs with sensitive data access and outbound connectivity, assess data exfiltration risks, evaluate potential for external influence through adversarial inputs, and implement data loss prevention controls appropriate for AI systems.
Maintain a comprehensive catalog of all AI services and SaaS platforms that use NHIs to access organizational resources and data. Tag each AI service with comprehensive metadata including model vendor, hosting vendor, deployment model (SaaS, self-hosted, cloud-managed), service type classification, and vendor trust assessment.
Assign and regularly update reputation scores for AI services and SaaS platforms based on data handling practices, security incident history, geographic and regulatory compliance, and use reputation scores to prioritize and remediate access policies violations.
Ensure AI services and LLMs operate only in pre-approved cloud regions that align with data residency and sovereignty requirements.
Log and analyze inbound and outbound access events, including authentication methods and frequency, consumer access patterns, unusual or anomalous authentication behavior, and cross-system access correlations. This ongoing monitoring complements the baseline consumer mapping established in control 1.3.
Monitor and log AI NHI outbound resource usage patterns including API call volumes and patterns, data access behaviors, resource consumption trends, and cost attribution and tracking.
Implement automated detection of unusual AI NHI behaviors including access pattern deviations, unexpected resource consumption, suspicious data access or transfer activities, overprivileged AI NHI accounts, and violations of data access boundaries. Cross-reference behavior with known threat indicators and established organizational policies.
Log all AI NHI-related activities in tamper-resistant audit logs, including access events, policy decisions, and lifecycle operations, with retention per organizational policy.
Prioritize AI NHI security controls and management actions based on risk assessment considering access patterns and privileges, data sensitivity exposure, policy compliance status, and behavioral anomaly indicators.
Monitor organizational AI NHI security maturity through KPIs such as percentage of AI NHIs with assigned ownership, credential rotation compliance rates, policy coverage and exception rates, mean time to detect and remediate violations, and innovation enablement metrics (balance between security and operational flexibility).
Establish feedback mechanisms to assess policy effectiveness, identify gaps in coverage or control, measure impact on business operations and innovation, and drive iterative framework improvements. Support automated or semi-automated remediation actions for policy violations, including credential revocation, rotation, and ownership reassignment workflows.
Real-World Implementation Examples
Simple SaaS AI Usage
Scenario: ChatGPT Web App for Conversation Only
In this straightforward scenario, a user accesses ChatGPT through a web browser for basic conversation without external integrations.
Access chart:
Framework Application:
- Pillar 1 (Discovery): Identify the service account representing the app in GCP.
- Pillar 2 (Ownership): Users or admins who provide Google OAuth consent to the application, along with vendor managers, are considered potential owners.
- Pillar 3 (Credential Lifecycle): OAuth delegated access utilizing short-lived credentials.
- Pillar 4 (Access Security): Corporate data is shared with OpenAI.
- Pillar 5 (Vendor Trust): Evaluate OpenAI as trusted and sanctioned service provider.
- Pillar 6 (Monitoring): Basic usage tracking and cost management.
- Pillar 7 (Risk Management): Evaluating whether sensitive data-sharing risks with OpenAI outweigh chatbot benefits.
Key Security Considerations: visibility to usage, data residency with OpenAI.
Desktop AI with Local and Remote Components
Scenario: Claude Desktop App with MCP Integration
In this example a Claude desktop application interacts with the user’s Outlook Calendar.
Access chart:
Framework Application:
- Pillar 1 (Discovery): Scan for desktop apps, configured tools and credentials; scan for MCP tools and configured credentials; identify the Service Account representing the Claude Desktop in GCP and the MCP in Azure. Associate them all to the agent.
- Pillar 2 (Ownership): Desktop users own their local apps and tools, along with any enterprise administrators.
- Pillar 3 (Credential Lifecycle): OAuth delegated access utilizing short-lived credentials.
- Pillar 4 (Access Security): Local MCP server security, limit internet outbound access to avoid calendar exfiltration.
- Pillar 5 (Vendor Trust): Anthropic and Microsoft as service providers.
- Pillar 6 (Monitoring): Desktop app usage, MCP server activity, calendar access patterns.
- Pillar 7 (Risk Management): Evaluating sensitive calendar data-sharing risks with Anthropic. Evaluate potentially accidental calendar actions.
Key Security Considerations: local service security, OAuth scope management.
Autonomous Agent with Multiple API Integrations
Scenario: LangGraph Agent for Sales Intelligence
This scenario shows a business intelligence autonomous agent that operates on a schedule. The agent accesses Gong to retrieve call transcripts, process them with OpenAI GPT model, then embeds them in an external vector database using a Google Gemini embedding model.
Access chart:
Framework Application:
- Pillar 1 (Discovery): Catalog all API keys (OpenAI, Google, Gong), database credentials, agent identity and its key store roles. Associate them all to the agent.
- Pillar 2 (Ownership): Assign owners for each service integration and the agent.
- Pillar 3 (Credential Lifecycle): Rotate API keys and database credentials, secure credential storage.
- Pillar 4 (Access Security): Rate limiting for APIs - model providers and Gong. Proper database access management. Limit internet outbound access to avoid transcripts exfiltration.
- Pillar 5 (Vendor Trust): Evaluate OpenAI, Google, and Gong as service providers.
- Pillar 6 (Monitoring): Track API consumption, processing costs, data flow patterns, anomalous behavior.
- Pillar 7 (Risk Management): Assess cumulative risk across the entire workflow.
Key Security Considerations: Long-lived API keys, automated execution risks, multi-vendor data flows, cost management across services.
Glossary
Malicious or carefully crafted input data designed to manipulate AI system behavior, extract sensitive information, or cause system failures.
An autonomous entity that performs tasks or interacts with other components within the AI ecosystem. Each agent is assigned with at least one model and can connect to other agents and access tools and services to carry out tasks.
An interaction where one agent accesses or communicates with another agent, requiring the source agent to authenticate or authorize itself to the target agent.
Digital credentials and authentication mechanisms used by AI systems to access resources, services, and data. This includes service accounts, API tokens, certificates, and other machine-to-machine authentication methods specifically associated with AI workloads.
A repository that stores information about IT assets and their relationships, used for tracking ownership and dependencies in IT environments.
The entity that initiates actions or requests within the AI ecosystem. This can be a human user, another agent, or standalone software.
Any storage or retrieval system that supports agent tasks, such as vector search engines, contextual search systems, or Retrieval-Augmented Generation (RAG) backends. Databases are typically accessed indirectly via tools or agents.
The unauthorized transfer of sensitive data from an organization's systems to external locations, often performed covertly by malicious actors.
Security technologies and processes designed to detect, monitor, and prevent unauthorized transmission of sensitive data.
Temporary authentication tokens with short lifespans, automatically generated and rotated to minimize security risks.
In relation to agents, refers to consumers that authenticate and initiate requests or actions toward the agent.
Authentication processes that occur between automated systems without human intervention, typically using API keys, certificates, or service accounts.
The intelligence component assigned to an agent that defines the agent’s behavior, capabilities, and reasoning logic. Models themselves do not act but serve as the decision-making foundation for agents.
A specific type of model designed to process, understand, and generate human-like text. LLMs serve as the reasoning and communication engine for agents that interact through language.
A type of attack where compromised API keys for language models are exploited to generate unauthorized costs and consume resources.
An authorization framework that enables applications to obtain limited access to user accounts or services without exposing user credentials.
A specific use of OAuth where an application or agent is granted permission to act on behalf of a user, accessing only the resources and actions the user has authorized, without sharing the user’s credentials.
In relation to agents, refers to agent tools, databases, and other agents utilized by the agent to perform its functions.
An account or identity that has been granted more permissions or access rights than necessary to perform its intended function.
The backend system or provider that a tool interacts with, including SaaS platforms, internal services, or other persistent software systems. Tools provide agents controlled access to services.
Unauthorized or unmanaged AI tools and services used within an organization without proper security review or governance oversight.
A software component or interface that exposes functionality to agents, including MCP servers, APIs, or internal code modules. Tools act as intermediaries between agents and services.
Security solutions that analyze patterns of user and entity behavior to detect anomalies and potential security threats.