Topic starter
06/06/2026 2:08 am
TL;DR: Teleport says AI agents are becoming vulnerable to both phishing-style manipulation and identity attacks, while 82% of executives surveyed by Capgemini plan to implement them within three years. Identity and access models built for humans or machines are no longer sufficient when runtime decision systems can be tricked into acting.
Editorial analysis by NHI Mgmt Group, based on content published by Teleport: “2025 Cybersecurity Predictions by Teleport CEO Ev Kontsevoy”.
Key questions
Q: What breaks when AI agents are not governed at runtime?
A: Without runtime governance, an agent can shift behaviour after provisioning and still execute actions that were never reviewed in context.
Q: Why do AI phishing attacks create more risk than traditional phishing?
A: AI lowers the cost, time, and skill needed to produce personalised lures, so attackers can run more campaigns and iterate faster.
Q: How should teams govern AI agent access to production data?
A: Treat agent actions as runtime events that need policy checks at execution time, not just pre-approved credentials.
Practitioner guidance
- Define an agent identity class Assign AI agents a governed identity category in IAM and IGA so they are inventoried, reviewed, and revoked as first-class subjects rather than treated as application logic.
- Scope agent access to task boundaries Bind each agent to narrowly scoped permissions, approved tools, and explicit execution limits so runtime action stays aligned to the task that justified access.
- Treat untrusted input as a control issue Review prompt injection and external content exposure as access-control risks, especially where agents can follow links, use tools, or surface data from connected systems.
Bottom line: AI agents create an identity governance problem because they do not fit cleanly into the traditional human-versus-machine split.
What's in the full article
Teleport's full article covers the operational detail this post intentionally leaves for the source:
- The article expands on the practical case for treating AI agents as a distinct identity governance problem, not just a software feature.
- It discusses how phishing-style manipulation can affect agent behaviour and why that changes the security model.
- It sets out Teleport’s view on consolidating identity controls across humans, machines, and agents.
- It includes the article's own framing for why passwordless and cryptographic authentication matter in this shift.
👉 Read Teleport's analysis of AI agent identity risk and IAM gaps →
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06/06/2026 3:38 am
AI agents expose an identity classification gap, not just a new security category. IAM programmes have long depended on a human-versus-machine split that no longer holds when an actor can reason, act, and be influenced in the same session. Once that split fails, governance has to treat the agent itself as the subject of control, not as a feature attached to software. Practitioners should stop asking whether agents fit existing boxes and start defining what box they occupy.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between passwordless authentication and AI agent governance?
A: Passwordless authentication reduces one class of credential exposure, but AI agent governance governs what the agent is allowed to do after it authenticates. In other words, strong authentication proves who or what the actor is, while governance constrains runtime behaviour, tool use, and delegation. Both matter, but they solve different problems.
👉 Read our full editorial: AI agent identity risk is outpacing enterprise IAM controls