Teleport recently unveiled the Teleport Agentic Identity Framework, a new AI-centered security model designed to help enterprises safely deploy autonomous and semi-autonomous AI agents across cloud and on-premises environments. The framework provides organizations with a roadmap for treating AI agents as trusted identities, addressing growing risks around data exposure, compliance failures, and adversarial threats as agentic AI rapidly moves into production.
The announcement comes as many infrastructure and security teams struggle to secure AI agents that operate continuously, invoke tools, and access sensitive systems without direct human oversight. Traditional identity and access models were not built for non-deterministic, always-on workloads. Teleport’s framework is intended to close that gap by defining the policies, reference architecture, and developer tooling needed to run AI agents at scale securely.
A recent Teleport survey of more than 200 infrastructure leaders highlights the urgency of the shift: 69% said widespread AI adoption will require major changes to identity management, while only 2% disagreed. Yet many organizations still rely on static secrets, hard-coded credentials, and custom integrations layered on legacy IAM and PAM systems, creating identity sprawl, limited visibility, and systemic risk.
Teleport’s Infrastructure Identity Platform underpins the framework with a unified, cryptographically secured identity layer backed by a hardware root of trust. It replaces static credentials with strong, ephemeral identities and enables zero-trust authentication, zero standing privileges, and real-time visibility into identity behavior across infrastructure. This approach is designed to reduce blast radius, prevent unauthorized access, and protect against identity-based attacks that often lead to data compromise.
“A unified identity layer is a prerequisite to deploying AI within enterprise infrastructure environments,” said Ev Kontsevoy, co-founder and CEO of Teleport. “Deploying AI on top of fragmented credentials and identity silos is a recipe for secrets and data leakage.”
Unlike point solutions that focus on LLM safety, runtime monitoring, or post-incident detection, Teleport’s framework elevates identity as the foundation of trust for agentic systems. It defines an opinionated security model built on cryptographic identity, ephemeral privileges, access guardrails, auditability, and real-time enforcement.
Industry analysts echo this shift. “As organizations deploy autonomous AI agents, identity – not monitoring – becomes the primary security control,” said Frank Dickson, Group Vice President, Security & Trust at IDC. “Without a unified identity foundation, agentic systems introduce unmanageable risk across data, infrastructure, and compliance.”
The Agentic Identity Framework is designed to help organizations accelerate secure AI adoption by standardizing practices, reducing the risk of credential leakage, and supporting compliance and governance mandates. It treats AI agents as first-class identities, adopts open standards such as MCP and SPIFFE, and unifies governance across agents, tools, and data through a controlled MCP and LLM layer for budgets, rate limits, and guardrails./p>
As AI-driven systems proliferate, Teleport positions identity as the missing control plane for agentic environments, anchoring security, reliability, and scale in a single, unified trust layer. It is certainly not alone in this ambition; however, it does offer some different approaches:
While platforms like Datadog, New Relic, and Splunk help teams observe AI agents and infrastructure through logs, metrics, traces, and anomaly detection, they are fundamentally reactive. They can highlight when an agent behaves unexpectedly or when a system is compromised, but they do not control what the agent is allowed to access or execute. In an agentic AI world, where software can autonomously call APIs, query databases, and modify systems, monitoring alone is insufficient. Teleport’s Agentic Identity Framework operates one layer deeper by enforcing who or what an agent is, what it can access, and for how long, using cryptographically verified, ephemeral identities rather than static credentials.
By contrast, HashiCorp Vault and Boundary focus on secrets management and privileged access, but still rely on issuing and rotating credentials that must be stored and referenced by applications or agents. This model becomes brittle at scale, especially for non-deterministic AI systems that spin up and down dynamically. Teleport eliminates long-lived secrets, replacing them with short-lived, identity-based access that is continuously validated and audited. Where observability tools show you what happened and secrets platforms help you manage credentials, Teleport’s framework aims to prevent unsafe access in the first place, making identity the primary control plane for agentic AI.
