How to Use Identity Management for AI Security

OAuth 2.0 is gaslighting your AI security team Agentic AI is not a user. It is not a service. It is not a device. Yet every identity and access control framework forces you to pretend it is. OAuth 2.0 was built for humans.  • Session-based tokens.  • Consent screens.  • User-driven scopes. That model starts to break down when your AI agents spin up other agents, authenticate across domains, and act autonomously on your behalf. Authorization becomes guesswork. Identity becomes a facade. Audit trails? Broken. You cannot revoke a session if the session has no owner. You cannot assign a role if the entity mutates its own purpose. What now? Start here: ✅ Bind AI agents to purpose-specific, least-privileged service identities. ✅ Scope access based on environmental context, not static roles. ✅ Enforce policy decision points outside of the agents themselves. ✅ Log everything—agent creation, intent, and action. This is not IAM. This is something else. What is your team doing to handle identity for AI agents? #AIsecurity #IdentityAccessManagement #AgenticAI #Cybersecurity

Reading the new Agentic AI Identity and Access Management report from the Cloud Security Alliance made me pause. It highlights something we often overlook. Thats the the fact that existing identity systems were never designed for autonomous agents. These agents do not just log in like humans or service accounts. They make decisions, interact across multiple systems, and act in ways that traditional IAM simply cannot handle. Key highlights from the report • Traditional protocols like OAuth, OIDC, and SAML fall short in multi-agent environments because they assume static identities and predictable workflows • AI agents require fine-grained, context-aware permissions that change in real time • Agent IDs based on Decentralized Identifiers and Verifiable Credentials allow provenance, accountability, and secure discovery • The proposed framework blends zero trust principles, decentralized identity, dynamic policy enforcement, authenticated delegation, and continuous monitoring • Concepts like ephemeral IDs, just-in-time credentials, and zero-knowledge proofs address the privacy and speed demands of autonomous systems Who should take note • Security leaders preparing for agent-driven enterprise systems • Engineers and architects designing secure frameworks for agent-to-agent communication • Product teams deploying agents into sensitive workflows • Governance leaders shaping accountability and compliance policies Why this matters Our identity models were built around human users and predictable software. Agentic AI changes that equation. Without new approaches, we risk security blind spots, accountability gaps, and over-privileged systems that cannot be traced or revoked in time. The path forward Enterprises need to start treating AI agents as first-class identities. That means verifiable credentials, continuous monitoring, and dynamic delegation as the baseline. This is not about adding more controls. It is about reshaping IAM so that trust, security, and accountability are preserved in the age of autonomous systems.

The proliferation of AI agents, particularly the rise of "shadow autonomy" presents a fundamental security challenge to the industry. While comprehensive controls for Agentic AI identities, Agentic AI applications, MCP, and RAG are discussed in the previous blogs, the core issue lies in determining the appropriate level of security for each agent type, rather than implementing every possible control everywhere. This is not a matter of convenience, but a critical security imperative. The foundational principle for a resilient AI system is to rigorously select a pattern that is commensurate with the agent’s complexity and the potential risk it introduces. These five patterns are the most widely used in agentic AI use cases, and identifying the right patterns or anti-patterns and controls is critical to adopting AI with necessary governance and security. 🟥 UNATTENDED SYSTEM AGENTS How It Works: Run without user consent, authenticated by system tokens. Risk: HIGH Use Cases: Background AI data processing, monitoring, data annotation, and event classification. Controls: ✅ Trusted event sources ✅ Read-only or data enrichment actions ✅ MTLS for strong auth ✅ Prompt injection guardrails Anti-Patterns: ❌ Access to untrusted inputs ❌ Arbitrary code/external calls 🟥 USER IMPERSONATION AGENTS How It Works: Act as a proxy with the user’s token (OAuth/JWT). Risk: HIGH Use Cases: Assistants retrieving knowledge, dashboards, low-risk workflows. Controls: ✅ Read-only or limited APIs ✅ Output guardrails ✅ MTLS Anti-Patterns: ❌ Write/state-changing ops ❌ Privileged APIs 🟨 ATTENDED SYSTEM AGENTS How It Works: Service identity with OAuth/API tokens, with human approval required. Risk: MEDIUM Use Cases: DevSec AI, privileged updates, infra changes. Controls: ✅ Explicit user approval ✅ Logging & audits ✅ MTLS Anti-Patterns: ❌ Blanket downstream access ❌ Unsafe ops (delete/shutdown) ❌ Unmanaged API escalation 🟩 USER DELEGATED AGENTS How It Works: OAuth 2.0 on-behalf-of token (OBO) exchange binds user + agent with consent and traceability. Risk: LOW Use Cases: Recommended for high-risk agent autonomy Controls: ✅ Time-bound consent ✅ Strict API scoping ✅ MTLS Anti-Patterns: ❌ Long-lived refresh tokens ❌ Write/state-changing ops 🟥 MULTI-AGENT SYSTEMS (MAS) How It Works: Multiple agents coordinate with dynamic identities. Hybrid + third-party. Risk: HIGH Use Cases: Decentralized AI with hybrid, in-house + vendor agents. Controls: ✅ Federated SSO ✅ MTLS for all comms ✅ Dynamic authorization ✅ Behavior monitoring ✅ MAS incident response Anti-Patterns: ❌ Static tokens ❌ No custody chain ❌ No secure framework ⚖️ BOTTOM LINE: Security controls must map to agent complexity and risk. From high-risk impersonation to low-risk delegated models with explicit consent and traceability, these patterns deliver proportionate controls, governance, and resilience in agentic AI adoption. #AgenticAI #AISecurity #ShadowAutonomy