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Table Of Contents

  • AI/MCP Concepts

Blended Identity

Blended Identity

Blended Identity refers to a modern identity model for user-driven AI agents in which the agent operates using a composite identity derived from two simultaneous sources of trust:

  • The agent’s own workload identity (cryptographically verifiable, rooted in a trust provider), and
  • The identity of the human user currently engaging or instructing the AI agent.
  • The combination produces a dynamic, runtime-only identity that shapes what the agent can do, enforces least privilege, and preserves full accountability for user-initiated actions taken by the agent.

What makes blended identity unique is that, although anchored in workload identity, it must also integrate with an enterprise’s workforce identity system to generate this composite identity just-in-time at the moment of invocation.

How Blended Identity Works

Technically, blended identity emerges when two identity planes, workforce IAM and workload IAM, are fused during an AI interaction:

  • The AI agent authenticates using its workload identity, validated through a Trust Provider such as Kubernetes SAs, GitHub OIDC, or cloud-native instance identity.
  • The human user authenticates through a workforce IdP (e.g., Okta, Azure AD) and initiates an action, request, or workflow.
  • At runtime, the system creates a combined identity context, binding:
    • the attested identity of the agent (e.g., “this is a real workload running in env X”), and
    • the verified human identity (e.g., “this action originates from a specific user with role Y”).
  • Authorization decisions are made using the blended identity, not the user or agent alone.
  • Access policies can incorporate both human attributes (group, role, MFA, department) and workload attributes (attestation, environment, repo, posture) for fine-grained governance.
  • Every downstream action the agent takes (API calls, cloud access, data retrieval, code execution) is logged with a full chain-of-custody tying machine actions back to human initiators.

Why Blended Identity Matters

AI agents increasingly function as autonomous extensions of human decision-making. Without blended identity:

  • Agents would either operate with independent permissions (dangerous) or rely on user credentials (even worse).
  • Enterprises would lose the ability to attribute actions such as data access, code pushes, or API calls to the initiating human.
  • Access would be either over-broad (“give the agent everything it may ever need”) or brittle (“break when the user changes roles”).
  • There would be no principled way to impose conditional access, posture requirements, or least-privilege limits on agent-driven actions.

Blended identity solves these problems by ensuring:

  • AI agents inherit only the subset of privileges appropriate for the current user and task.
  • Enterprises maintain continuous accountability across user → agent → workload → resource chains.
  • Access conforms to zero trust principles, using identity and context, not static keys, tokens, or implicit trust.

This model becomes essential as organizations adopt AI copilots, autonomous agents, and agentic workflows that must safely interact with internal systems, APIs, and cloud environments.

Common Challenges with Blended Identity

  • Coupling workforce IAM and workload IAM: These two systems operate differently and rarely integrate natively, requiring reconciliation of human attributes with workload attestation.
  • Dynamic delegation chains: User → agent → workload → API interactions must all be captured without collapsing identity into a single undifferentiated token.
  • Real-time identity binding: Blended identity must be created at invocation time, not pre-provisioned, which requires low-latency trust evaluation and credential issuance.
  • Policy ambiguity: Organizations must decide:
    • What comes from the user?
    • What comes from the agent?
    • What happens when either identity’s permissions change?
  • Audit complexity: Compliance frameworks require precise attribution of machine-driven actions to specific users, which is impossible without explicit identity blending.

How Aembit Helps

Aembit operationalizes blended identity by joining workload identity attestation with workforce identity signals inside its Workload IAM authorization model.

Here’s how:

1. Verified Workload Identity (the agent’s root identity)

Aembit integrates with trust providers, AWS, Azure, GCP, Kubernetes, GitHub Actions, to verify the AI agent’s identity using cryptographic signals rather than static secrets.

2. Human Identity Inheritance & Session Binding

When a user prompts or instructs an AI agent, Aembit binds the user’s verified identity (via the organization’s Workforce IDP) to the agent’s workload identity, creating a run-time blended identity.

3. Policy Enforcement Based on Both Identities

Policies can reference:

  • human roles, groups, risk signals, session context
  • workload attributes, posture, environment, repo, runtime attestation

This enables least-privilege access that adapts to both sides of the identity equation.

4. Short-lived and Secretless Access

The agent never stores or handles long-lived secrets. Aembit injects workload-scoped, user-scoped credentials JIT, eliminating secrets sprawl while ensuring full accountability.

5. Complete Chain-of-Custody Auditing

Aembit logs each access event with:

  • the human identity
  • the agent’s workload identity
  • policy conditions
  • credential issuance & expiration
  • target resource

This provides transparent auditability across human → agent → resource access flows.

Related Reading

FAQ

You Have Questions?
We Have Answers.

How is blended identity different from delegation or impersonation?

Delegation passes a user’s authority to another system. Blended identity never lets the agent impersonate a user; it combines identities so policies can evaluate both independently.

Because agents act on behalf of humans, but using their own execution environment and workload identity. Without blending, neither accountability nor least privilege is possible.

No. It builds on both:

  • Workforce IAM proves who the user is.
  • Workload IAM proves what the agent is.

Blended identity is the runtime fusion that governs agent-driven actions.

Aembit ties the user session to the agent’s workload identity and enforces policy at the moment of access, recording a full, verifiable chain across all hops.