Pindrop Comment on NIST NCCoE Concept Paper

Accelerating the Adoption of Software and AI Agent Identity and Authorization

April 1, 2026
VIA EMAIL ([email protected])
National Institute of Standards and Technology
National Cybersecurity Center of Excellence (NCCoE)

Re: NIST NCCoE Concept Paper – Accelerating the Adoption of Software and AI Agent Identity and Authorization

Dear Sir or Madam:
Pindrop Security, Inc. (“Pindrop”) appreciates the opportunity to provide input on the National Cybersecurity Center of Excellence (NCCoE) concept paper regarding software and AI agent identity and authorization. Our comments reflect Pindrop’s technical, security, and governance perspectives on advancing practical, implementation-oriented approaches to identity, authentication, and authorization in agentic systems.

Authors

Elie Khoury
Senior Vice President, Research
Pindrop Security, Inc.

Clarissa Cerda
Chief Legal Officer & Secretary
Pindrop Security, Inc.
1115 Howell Mill Rd, Suite 700
Atlanta, GA 30318

Executive summary

Pindrop appreciates the opportunity to provide input on NIST’s concept paper regarding software and AI agent identity and authorization. We strongly support the NCCoE’s emphasis on practical, implementation-oriented application of existing identity, authentication, and authorization standards (for example: OAuth 2.0/2.1, OpenID Connect, SPIFFE/SPIRE, SCIM, and NGAC) to agentic architectures.

As agentic systems scale, achieving accountable agentic systems requires resolving a core challenge: delegated AI accountability—ensuring that actions taken by software and AI agents on behalf of humans remain attributable, verifiable, and governed. This requires extending identity and authorization frameworks beyond static credentials to incorporate contextual, behavioral, and interaction-derived signals as inputs to authorization decisions. In high-assurance environments, these signals represent a necessary extension of identity and authorization systems, reflecting a shift from static credential-based trust to continuously evaluated, signal-driven authorization decisions.

Our perspective is informed by experience securing high-risk interaction channels (e.g., contact centers and virtual meetings), deploying multi-factor risk assessment for fraud and authentication, and operating agentic systems that assist with post-interaction investigations. A key concept is the delegation provenance chain, which enables traceability between human authorization events and subsequent agent actions.

From this vantage point, we highlight four themes that may strengthen the practical implementation guidance envisioned for the proposed project:

  • Human authenticity in delegation workflows. As agents act on behalf of users, systems must verify not only which agent is acting, but which human authorized the action and whether that authorization reflects a genuine human presence rather than synthetic impersonation.
  • Differentiating good bots from bad bots (authorized vs malicious or compromised agents). Enterprises must distinguish legitimate, governed automation from adversarial or compromised agents using identity, behavior, and contextual signals.
  • Layered identity assurance incorporating behavioral and contextual signals. Effective governance requires combining workload identity, human authorization assurance, contextual risk signals, and adversarial detection into a unified model.
  • Grounding in real-world, high-risk agent use cases. Existing deployments demonstrate the feasibility of combining strong workload identity, human-in-the-loop controls, and deep observability; practical implementations should demonstrate how these controls operate in high-risk, human-facing environments where fraud, security, and compliance risks are material.

1. Context and Alignment with NIST’s Areas of Interest

The concept paper correctly notes that as software and AI agents increase autonomy and scale, foundational identity principles—identification, authentication, and authorization—must ensure agents are known, trusted, and properly governed.

From an implementation and governance perspective, these challenges converge on a central requirement: enabling accountable agentic systems in which identity, authorization, and delegation decisions remain continuously verifiable and auditable.

The paper’s areas of interest map closely to operational challenges observed in deployments:

  • Distinctly identifying agents while binding them to accountable operators and human governance structures
  • Authenticating agents consistent with modern cloud infrastructure and managed AI services
  • Authorizing agents under zero-trust, least-privilege assumptions despite partly unpredictable behavior
  • Delegating access so human users can empower agents while preserving accountability and revocability
  • Logging and auditing to reconstruct agent actions, data access, and decision-making

We value NIST’s intent to produce a practice guide with implementation details and enterprise use-case focus where organizations can exert control over agents and systems.

2. Human Authenticity in Delegation and “On Behalf Of” Workflows

From a governance perspective, the integrity of the human authorization event becomes the root of trust for all downstream agent actions and is foundational to achieving delegated AI accountability.

The concept paper asks how to handle delegation for “on behalf of” scenarios and how to bind agent identity to human identity for human-in-the-loop authorizations. From our experience, the integrity of the human authorization event becomes a critical control point.

Key operational questions in high-risk workflows include:

  • Is this a machine?
  • Is this a fraudster?
  • Is this a valid delegation from our actual customer or employee?

NIST’s current framing addresses the first two primarily at the software level. We recommend explicitly addressing human authenticity whenever authority is delegated to an agent. Advances in synthetic media mean human authorization steps (voice calls, virtual meetings) can be convincingly simulated unless robust liveness and deepfake detection are used.

In interaction-driven workflows (e.g., voice or other real-time interfaces), signals derived from the interaction itself should be treated as required inputs for determining authenticity and authorization risk in high-assurance or adversarial scenarios.

Recommendations for Human Authenticity

a. Treat human authorization events as first-class identity artifacts

Identity frameworks should represent a human authorization event with:

  • Human identity, authentication method, and assurance level (aligned to SP 800-63-4)
  • Modality-specific authenticity evidence (e.g., multi-factor checks, behavioral/device risk, deepfake detection signals when audio/video are involved)
  • Contextual metadata (location, time, risk posture, policy references)

These authenticity signals should serve as inputs into authorization decisions and may be necessary to determine whether delegated actions should be permitted.

b. Define a “delegation provenance chain”

For “on behalf of” scenarios, systems should demonstrate how to link:

  • A high-assurance human authorization event
  • A scoped delegation policy (which agents may act, in what roles, over which resources, and for how long)
  • Subsequent agent actions and decisions

This chain enables non-repudiation and forensic reconstruction and can be implemented through linkage between identity assertions, policy artifacts, and audit logs within the agent execution lifecycle.

c. Incorporate synthetic impersonation into assurance thinking

Extend assurance levels to account for robustness against synthetic and deepfake-based impersonation, particularly where human presence is inferred from audio or video. Without this, robust machine identity can be misused by unauthenticated or adversarial actors.

3. Differentiating Good Bots from Bad Bots (Authorized vs Malicious or Compromised Agents)

A major practical theme is the need to distinguish legitimate automation from malicious or compromised automation. Enterprises rely on many automated agents while adversaries deploy automated systems that probe, attack, and commit fraud at scale.

The key question is no longer simply whether an entity is a bot, but whether it is an authorized, governed agent or an adversarial one.

This distinction is foundational to authorization decisions in adversarial environments, where static credentials alone are insufficient to establish trust.

Recommendations for Agent Differentiation

a. Extend agent identity metadata to capture legitimacy and operator context

Agent identity should include:

  • Agent type (e.g., human user, enterprise automation, third-party service, customer-controlled agent)
  • Operator of record (the accountable organization)
  • Legitimacy state (attested, under investigation, revoked)

These attributes should be managed through provisioning systems and incorporated into attribute-based access control decisions.

b. Recognize reputation and consortium models

Analogous to phone-number reputation, shared signals about automated agents (known legitimate provider bots, widely abused fraud bots, synthetic engines associated with abuse) can enhance trust decisions and risk scoring. This may include:

  • Per-agent risk scores used in authorization policies
  • Federation or consortium models allowing organizations to vouch for agent classes

c. Incorporate dynamic behavior and content signals into authorization

Static credentials are insufficient. Systems should incorporate:

  • Behavioral analytics (e.g., anomalous API or interaction patterns, cross-tenant clustering)
  • Content-aware indicators (fraud scripts, social engineering patterns, automated probing)
  • Policy violations, lifecycle state changes, or integrity signals
  • Interaction-derived signals reflecting authenticity, intent, and real-time engagement context

These signals should be treated as first-class inputs to authorization decisions, particularly where static credentials are insufficient to establish trust.

Trust is not established by valid credentials alone, but by whether an agent should be trusted for a given action, in a given context, at a given time.

4. Recommendations Aligned to NIST’s Question Areas

These implementation approaches support the development of accountable agentic systems by ensuring identity, authorization, and delegation controls remain continuously verifiable and context-aware.

Identification

  • Demonstrate workload identity standards and cloud-native identity providers to establish cryptographically strong identities for agents
  • Define an application-layer agent registry capturing:
    • purpose
    • capabilities (tools, data domains)
    • risk class
    • operator
    • associated humans
    • relationships to other agents

This dual approach separates infrastructure identity from application-layer governance metadata while enabling strong binding between the two.

Authentication

  • Use short-lived, scoped credentials (e.g., OAuth 2.1 access tokens scoped to resources)
  • Combine with transport-level protections such as mutual TLS
  • Incorporate attestation mechanisms that include statements about runtime environment, configuration, or software supply chain (e.g., “this agent runs a vetted container on a hardened node”)

Authentication mechanisms should produce verifiable assertions that can be evaluated alongside contextual, behavioral, and interaction-derived signals during authorization.

Authorization and Delegation

  • Enable event-driven policy updates when an agent’s context changes (e.g., access to new tools, changes in data sensitivity, or behavioral indicators of risk)
  • Bind human approvals to agent permissions through explicit delegation artifacts, including multi-factor human verification, scoped permissions, and defined revocation conditions
  • Separate baseline agent entitlements from task-specific delegated permissions

In practice, agent context may evolve dynamically—for example, when an agent gains access to new tools, operates over aggregated data with increased sensitivity, or exhibits behavior that suggests potential compromise. As a result, authorization policies must be capable of adapting in real time under least-privilege and zero trust assumptions.

Authorization decisions may incorporate contextual, behavioral, and interaction-derived signals as inputs to policy evaluation, particularly in high-risk workflows, supporting dynamic enforcement of least privilege and adaptive trust.

Auditing, Non-Repudiation, and Prompt Injection

  • Define standardized logging schemas that correlate:
    • human identity and assurance
    • agent identity and configuration
    • prompts, tool invocations, and relevant context
    • decisions and outputs
  • Ensure logs are tamper-resistant and support non-repudiation and forensic reconstruction
  • Model guardrails and content filtering should be implemented to mitigate prompt injection and unsafe outputs, with outputs and decisions captured for evaluation of safety, factuality, and policy alignment.
  • Preserve linkage across human authorization events, delegation artifacts, agent identity, and resulting actions to support end-to-end traceability

5. Policy and Governance Considerations

Effective agentic systems require governance models that translate identity and authorization into enforceable, auditable controls. The following principles define key properties of accountable agentic systems:

In high-risk or adversarial environments, these controls may depend on signals derived from real-time interactions to establish authenticity, intent, and trustworthiness.

  • Authenticity as a Foundational Control
    Trust depends on the authenticity of human authorization events. Systems must distinguish genuine human presence from synthetic or spoofed interactions. This can be implemented through structured authorization artifacts incorporating identity assurance levels and interaction-derived authenticity signals.
  • Risk-Proportional Authorization
    Authorization decisions must be risk-proportional and adaptive. Systems should dynamically adjust controls based on contextual, behavioral, and interaction-derived signals through event-driven policy evaluation and step-up verification.
  • Accountability Through Verifiable Evidence
    Accountability must be anchored in durable, verifiable evidence linking human authorization, agent identity, and resulting actions. This can be implemented through linked logging of authorization artifacts, delegation policies, and agent activity.
  • Dynamic and Context-Aware Trust
    Trust must be continuously evaluated rather than statically granted. Authorization decisions should adapt based on behavior, context, and real-time interaction signals, which may be necessary to establish authenticity and intent in high-risk scenarios.
  • Clear Ownership and Lifecycle Accountability
    Agent identity must be tied to an accountable operator and governed across its lifecycle, including creation, modification, suspension, and revocation.
  • Technology Neutrality and Interoperability
    Governance approaches should remain standards-based and interoperable, leveraging existing enterprise identity frameworks.

6. Conclusion

Pindrop strongly supports NCCoE’s goal of producing practical, implementation-oriented guidance on identity and authorization for software and AI agents.

Achieving accountable agentic systems requires addressing the core challenge of delegated AI accountability, ensuring that actions taken by agents on behalf of humans remain attributable, verifiable, and governed.

We recommend the project:

  • Treat human authenticity and synthetic impersonation risk as central to delegation workflows
  • Differentiate good bots from bad bots (authorized vs malicious or compromised agents) using contextual, behavioral, and interaction-derived signals
  • Model layered identity assurance within zero trust architectures
  • Ground demonstrations in high-risk, real-world scenarios where agentic systems can materially impact fraud, security, financial outcomes, or user trust

We welcome the opportunity to support NCCoE through participation in pilot implementations or demonstration activities, contributing insights from real-world deployments of agent identity and authorization controls.