AI Security for the Age of Autonomous Systems
Secure AI agents and MCP toolchains with cryptographic identity. Smallstep replaces API keys and implicit trust with short-lived, hardware-backed certificates that prove what is acting, where it runs, and what it’s allowed to access.
The AI Security Problem
Non-human actors
AI agents, MCP clients, MCP servers, and model runtimes act independently of users.
Tool aggregation risk (MCP)
MCP servers aggregate powerful internal tools and data behind a single interface, increasing blast radius.
Unverifiable access
No cryptographic proof of which device, workload, or MCP endpoint initiated a request.
Credential sprawl
API keys and tokens end up embedded in code, prompts, notebooks, pipelines, and CI logs.
Invisible lateral movement
Models call MCP servers. MCP servers call tools. Tools call internal APIs and data stores.
Shadow AI access
Unmanaged devices silently gain access to AI tools and MCP-exposed services.
Hardware-Bound Trust
ACME device attestation establishes cryptographic trust anchored directly in hardware. Devices prove their identity using secure elements such as TPMs or Secure Enclaves before certificates are issued, eliminating reliance on reusable secrets. This ensures only verified, uncompromised devices can authenticate, even in zero-trust and automated environments.
Identity Control Plane
The identity control plane centralizes how devices, workloads, and services are identified, authenticated, and authorized. It replaces fragmented credential handling with a single system of record for cryptographic identity, policy, and lifecycle enforcement. This enables consistent trust decisions across infrastructure, automation, and emerging AI-driven systems.
Transparent Authentication
Selective ZTNA enables access decisions without interrupting users or workflows. Authentication occurs continuously and contextually, based on device identity, posture, and policy, rather than explicit login events. This allows access controls to remain enforced while reducing friction for users and automated systems alike.
API keys break in autonomous AI systems
AI agents and MCP toolchains rely on portable, long-lived secrets. API keys cannot prove execution context, cannot be scoped to devices or workloads, and cannot be trusted once copied or reused.
| API Keys | Certificates | |
|---|---|---|
| Credential lifetime | Long-lived | Short-lived |
| Portability & theft risk | Copyable | Copyable Non-exportable |
| Identity provenance | No provenance | Cryptographically verifiable |
| Rotation | Hard to rotate | Automatically rotated |
| System model alignment | Human-centric | AI and MCP ready |
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Integrates with your existing security stack
The platform integrates with existing identity providers, infrastructure, and security tooling, including AI runtimes and MCP-based systems. It extends cryptographic identity and policy enforcement to agents, tools, and automated workflows without requiring architectural replacement. This allows trust controls to remain consistent as execution shifts from users and services to autonomous systems.
The Foundation for Secure AI
AI systems are becoming autonomous participants in your infrastructure. Security must evolve from who logged in to what is acting. Smallstep provides the cryptographic identity layer that makes AI and MCP security real.