RSA Conference 2026 · Innovation Sandbox Finalist
Walseth AI vs Token Security: NHI Identity Security vs Behavioral Enforcement
Token Security is an RSAC 2026 Innovation Sandbox finalist presenting on Day 1 (March 23). They raised $28M in Series A funding led by Notable Capital and focus on AI agent and non-human identity (NHI) security. Their approach secures agent identities. Ours prevents agent misbehavior. Here is how the two approaches compare and why enterprises need both.
Token Security secures agent identities. We prevent agent misbehavior.
Identity governance and behavioral enforcement are two different layers of AI agent security. One controls who agents are. The other controls what they do.
Head-to-Head Comparison
| Dimension | Walseth AI | Token Security |
|---|---|---|
| Governance Layer | Behavioral enforcement. Governs what agents do and how they comply with policies at the code level. | Identity and NHI security. Governs who agents are, maps their permissions, and detects identity drift. |
| Key Capabilities | 5-level enforcement ladder, context integrity checks, constraint automation, compliance evidence generation. | Contextual identity graph, NHI discovery, permission drift detection, MCP server integration, behavioral anomaly detection. |
| Core Approach | Prevent-by-construction. Structural constraints eliminate violation classes before runtime. | Machine-first identity security. Continuous discovery and intent-based access controls for non-human identities. |
| What It Prevents | Behavioral violations, context drift, constraint regression, governance gaps in agent code. | Unauthorized agent access, permission sprawl, identity lifecycle gaps, NHI credential exposure. |
| Deployment Model | CI/CD integration. Hooks, tests, and templates enforced in the development pipeline. | Runtime identity platform. Continuous discovery and monitoring of AI agents and NHIs across infrastructure. |
| Target Buyer | Engineering leads, AI ops, compliance teams building agent systems. | CISO, identity security teams, NHI governance programs. |
| Pricing | Free scanner. $497 full report. $3K/month retainer. | Enterprise contract, not publicly disclosed. |
| Funding & Stage | Bootstrapped. Revenue-generating. | $28M Series A (Notable Capital, January 2026). RSAC 2026 Innovation Sandbox finalist. |
Who Is Token Security: Machine-First Identity for AI Agents
Token Security is building identity security purpose-built for non-human identities. Their thesis: AI agents will significantly outnumber humans in the enterprise, and traditional human-centric IAM was never designed to manage machine identities at this scale. They were selected as one of ten finalists for the RSAC 2026 Innovation Sandbox, presenting on Day 1 (March 23).
Their platform provides continuous discovery of NHIs across cloud infrastructure, a contextual identity graph that maps relationships between agents, services, and resources, intent-based access controls, and permission drift detection that catches when agent permissions deviate from their intended scope. They also integrate with MCP servers for agent toolchain visibility.
The Innovation Sandbox selection is significant. Finalists receive massive press coverage and visibility during RSA week. Token Security's content marketing is also sophisticated -- they published 10+ blog posts in a single week leading up to RSA, demonstrating strong go-to-market execution alongside their technical platform.
The Identity-Behavioral Gap: Two Layers of Agent Security
Identity governance answers: Who is this agent and what can it access? Behavioral enforcement answers: What will this agent do and how do we prevent violations? These are fundamentally different questions, and solving one does not solve the other.
An agent can be fully discovered in Token Security's identity graph, have correct permissions, and pass every NHI compliance check -- and still produce outputs that violate policies, drift from its constraints, or introduce governance regressions in the codebase it modifies. Identity verification ensures the right agent has the right access. It does not ensure the agent behaves correctly with that access.
This is the layer gap that enterprises experience. Permission controls answer who can act. Behavioral enforcement answers what actions are structurally possible. Read more about why detection-based approaches miss this layer in Why Detection-Based AI Governance Fails.
Complementary, Not Competitive: The Full Governance Stack
The strongest AI agent security posture uses both layers. Token Security's NHI governance ensures that every agent is discovered, every permission is mapped, and every identity drift is detected. Behavioral enforcement ensures that discovered, authorized agents actually comply with your policies, maintain context integrity, and produce outputs that meet governance standards.
Think of it as a building with both access control and safety systems. Token Security is the access control system -- it knows every person (and machine) in the building and what rooms they can enter. Walseth AI is the safety system inside each room -- it prevents dangerous actions regardless of who is authorized to be there.
Our enforcement ladder operates at five levels -- from prose documentation (L1) through automated hooks (L5) -- each compounding on the previous. This prevent-by-construction approach eliminates violation classes before they reach runtime. Learn how it maps to compliance frameworks in How the Enforcement Ladder Maps to NIST AI RMF.
When to Choose Each Approach
Choose Token Security when your primary concern is discovering and managing non-human identities across your infrastructure, you need to map agent-to-resource relationships via identity graphs, you want permission drift detection for AI agents at scale, or you need to prove NHI compliance for audit and regulatory requirements.
Choose Walseth AI when you are building AI agent systems and need governance embedded in the development process, you want to prevent behavioral violations before they reach production rather than detecting identity drift after deployment, you need compliance evidence that traces directly to enforcement actions, or you want governance costs that scale with constraints rather than agent count.
Use both when you need full-stack AI agent governance: NHI identity management for who agents are, behavioral enforcement for what agents do. Identity without behavioral constraints leaves a governance gap. Behavioral constraints without identity management leaves a visibility gap. Read our full analysis of Token Security's Innovation Sandbox selection.
See behavioral enforcement in action
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