NIST’s AI Agent Identity Paper Validates the Oktsec Thesis

NIST’s National Cybersecurity Center of Excellence (NCCoE) published a draft concept paper in February 2026: “Accelerating the Adoption of Software and AI Agent Identity and Authorization.” The paper asks how foundational identity principles can apply to AI agents to provide appropriate protections while enabling business value.

The paper asks six categories of questions. Oktsec already implements answers to all of them.

This is not a retroactive claim. The architecture decisions behind Oktsec — Ed25519 identity, YAML-based policy enforcement, in-process content scanning, SQLite audit trail — were made because the problem NIST is now formalizing was already visible in production. When agents communicate with agents, the security infrastructure needs to operate at the same layer.

The NIST framework

The paper identifies six areas of interest:

1. Identification — How are agents identified? What metadata is essential?
2. Authentication — What constitutes strong authentication for an agent?
3. Authorization — How do zero-trust principles apply to agent authorization?
4. Auditing — How do we ensure tamper-proof, non-repudiable logging?
5. Prompt injection — What controls prevent and mitigate injection attacks?
6. Data flow tracking — How do we maintain provenance of inputs and outputs?

The paper references MCP, OAuth 2.0/2.1, OpenID Connect, SPIFFE/SPIRE, SCIM, and NGAC as relevant standards. It also cites NIST SP 800-207 (Zero Trust Architecture), SP 800-63-4 (Digital Identity Guidelines), and NISTIR 8587 (Protecting Tokens).

How Oktsec maps to each area

NIST area	NIST question	Oktsec implementation
Identification	What metadata is essential?	Ed25519 public key + agent name. Key pair generated at initialization via oktsec keygen.
Authentication	What constitutes strong auth?	Every message signed with Ed25519 private key. Proxy verifies signature before processing. No shared secrets.
Authorization	How to enforce least privilege?	YAML policy files: per-agent, per-tool, per-target rules. Default-deny. Dynamic policy updates without restart.
Auditing	How to ensure tamper-proof logs?	SQLite audit trail (WAL mode). Structured events with agent ID, action, tool, parameters, verdict, timestamp.
Prompt injection	What controls prevent injection?	Aguara in-process scanning: 148 rules including 15 for prompt injection. Content scanned before delivery.
Data flow tracking	How to maintain provenance?	Full message chain: origin agent → proxy verdict → tool invocation → response → delivery. SARIF output for CI.

Why this matters for the market

Federal validation creates enterprise demand

When NIST publishes guidance, federal agencies follow it. When agencies follow it, government contractors adopt it. When contractors adopt it, the enterprise market normalizes it. This is the compliance cascade that turned SP 800-207 (Zero Trust) from a concept paper into a procurement requirement within three years.

The NIST concept paper on AI agent identity is the starting signal for the same cascade. Organizations that have agent identity and authorization infrastructure in place before the guidance finalizes will be positioned to capture enterprise demand. Organizations that wait will be retrofitting.

The paper explicitly references MCP

NIST lists the Model Context Protocol alongside OAuth and OIDC as a relevant standard for agent-to-service communication. This is significant. MCP is moving from a development tool to a standards-track protocol recognized by federal institutions. The security layer for MCP — what Aguara provides for static analysis and Oktsec provides for runtime enforcement — becomes a compliance requirement, not a nice-to-have.

The scope gap is our opportunity

The paper explicitly scopes to enterprise agents in controlled environments. It excludes “identifying and managing access for external agents from untrusted sources.” But the real-world deployment pattern includes agents installing MCP servers from public registries with tens of thousands of unverified tools.

This gap — between NIST’s enterprise scope and the actual threat surface — is exactly where the Aguara/Oktsec stack operates. Aguara Watch scans 42,655 skills across 7 public registries. The Kali MCP integration demonstrates that agents can invoke offensive security tools through the protocol. Static analysis before installation addresses the untrusted-source problem that NIST defers to future work.

The three use cases NIST proposes

The paper identifies three potential demonstration use cases:

1. Enterprise AI agents for workforce efficiency — calendar management, document generation, decision recommendations. Agents need delegated access to data sources.
2. Enterprise AI agents for security — agents analyzing security data and recommending/taking actions. Elevated risk due to sensitivity of security data.
3. Enterprise AI agents for software development — automated processes for CI/CD pipelines using AI agents.

All three use cases require the same security infrastructure: identity verification (who is this agent?), policy enforcement (what is this agent allowed to do?), content scanning (is this agent accessing something malicious?), and audit trails (what did this agent actually do?).

Oktsec addresses all three. The security use case is particularly relevant — when agents operate on security-sensitive data, the proxy’s Ed25519 identity verification and policy enforcement become essential rather than optional.

Standards alignment

NIST’s paper references six standards/frameworks. Here is how the Oktsec stack aligns:

Standard	NIST role	Oktsec alignment
MCP	Agent-to-service protocol	Aguara scans MCP tool definitions. Aguara MCP exposes scanning as MCP tool. Oktsec proxies MCP communication.
OAuth 2.0/2.1	Authorization tokens	MCP spec integrates OAuth for server auth. Oktsec complements with message-level Ed25519 signatures.
SPIFFE/SPIRE	Workload identity	Ed25519 key pairs serve same function: per-workload identity, attestation, and verification.
SP 800-207	Zero Trust Architecture	Default-deny policies. Every message verified. Never trust, always verify. Least privilege per tool.
NGAC	Fine-grained access control	YAML policies with per-agent, per-tool, per-target granularity. Per-rule action overrides.
SCIM	Identity lifecycle management	Agent keys managed via oktsec keygen (create), keystore removal (revoke). Future: SCIM API integration.

The timeline

The public comment period closes April 2, 2026. After that, NIST will consider a demonstration project. If the project proceeds, it will produce a practice guide with reference implementations.

This timeline matters for market positioning:

• Now – April 2026: Public comment period. Opportunity to influence scope and priorities.
• Mid-2026: NIST evaluates feedback, decides on demonstration project. Enterprise awareness of agent identity requirements grows.
• Late 2026 – 2027: If demonstration proceeds, NIST builds reference implementations. Early adopters of agent security infrastructure gain compliance positioning.
• 2027+: Practice guide published. Federal procurement requirements follow. Enterprise compliance cascade begins.

The organizations that build agent identity and authorization infrastructure now will be referenced in the NIST practice guide. The organizations that wait will be implementing the practice guide’s recommendations after the fact.

What to do

If you are building or deploying AI agents in enterprise environments:

1. Submit comments to AI-Identity@nist.gov before April 2. Reference the open-source implementations that already address their questions.
2. Implement agent identity now. Do not wait for the practice guide. Ed25519 key pairs, policy enforcement, and audit trails are available today.
3. Scan before you trust. Every MCP server your agents connect to is a trust decision. Aguara provides the static analysis layer.

NIST is asking the right questions. The answers already exist as open-source implementations. The opportunity is to influence the standards process while building the infrastructure that will become the reference.