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AI Agent and Machine Identity Security

IDENT1TY secures the identities and access of your AI agents and machine identities, so automation never becomes a vulnerability. We map, govern, and control every non-human identity, from service accounts to autonomous AI agents, with least privilege and full traceability.

What is machine identity security?

Machine identity security is the discipline that protects and governs non-human identities, the service accounts, API keys, tokens, workloads, and AI agents that authenticate and act across systems without a human behind the keyboard. It extends identity security beyond people to the fast growing population of machines and autonomous agents that now run most enterprise operations.

 

A machine identity, or non-human identity (NHI), is any credential that lets a machine talk to infrastructure: an API key, an OAuth token, an SSH key, a workload certificate, or the identity of an AI agent. Unlike human identities, machines cannot perform MFA, they operate 24/7 with no normal behaviour pattern, and they often persist indefinitely without lifecycle management, which makes them inherently harder to secure.

 

AI agents are the most critical subset. They are not passive credential holders, they are autonomous actors that acquire permissions at runtime, call external APIs, spawn sub-agents, and chain actions across systems. 

 

This expands the blast radius of any single compromised credential far beyond what a static service account could reach, which is why machine identity security has become a defining priority for 2026.

Machine identities, NHIs, and AI agents: what they mean

These terms overlap and are often used loosely. Here is how they relate.

Non-human identity (NHI) is the umbrella term for any identity that is not a person: service accounts, API keys, OAuth tokens, SSH keys, workload certificates, and bots.

 

Machine identity is used interchangeably with NHI, emphasizing the credentials that let workloads, applications, and devices authenticate to each other.

AI agent identity is the newest and fastest growing category of NHI: the identity of an autonomous system that reasons, decides, and acts across systems on behalf of users or other systems.

 

The key distinction is behaviour. A traditional NHI is static: you can enumerate, scope, rotate, and revoke it. An AI agent is dynamic: it can request new permissions and act in sequences no policy anticipated. Securing the first is an inventory problem. Securing the second is a governance problem, and it is the one most organizations are least prepared for.

 

Why choose an IAM integrator over a single vendor

Most vendors sell you a tool to vault secrets. But securing machine and AI agent identities is not a vaulting problem, it is a governance problem: no ownership, no lifecycle, no visibility into what an agent actually does after access is granted.

 

As a vendor neutral integrator, IDENT1TY delivers the governance layer that tooling alone cannot. We map every AI agent and machine identity across your environment, including the undeclared ones, define ownership and lifecycle rules, and align AI agent governance with your existing IAM, IGA, PAM, and cloud security policies.

 

That means you get:

  • Full visibility of every non-human identity, official, experimental, and undeclared, mapped and classified.
  • A governance model specifying the owner, scope, risk level, and purpose of each agent, with least privilege enforced.
  • Alignment with your IAM, IGA, PAM, and machine identity policies, plus audit ready traceability for DORA and sector regulators.

 

A pure vendor secures the credential. An integrator governs the identity, and its behaviour, over time.

Why most AI projects fail?

Uncontrolled access rights

Exposure to cyber threats, non-compliance with NIS2/ISO27001

Projects abandoned halfway

Costs explode, results never achieved

Undersized teams

Delays, vendor dependency, operational risks

How IDENT1TY secures AI identities where others fall short

AI agents generate thousands of non-human identities invisible to traditional solutions. Our approach was built for this challenge from day one.

Total visibility on non-human identities

Automatic mapping of every AI agent, service account and API token, including those created dynamically at runtime.

Lifecycle-adapted governance

Access policies aligned with AI workload ephemerality: just-in-time access, automatic secret rotation, immediate revocation.

Multi-vendor independence

Compatible with OpenAI, Azure AI, Bedrock, Vertex AI and any on-premise model. No lock-in to a proprietary ecosystem.

Measurable results within 30 days

Quantified attack surface reduction, CISO and auditor-ready reporting included in every engagement.

What we do today

We help organizations identify, classify and govern AI agents as fully fledged digital identities in their own right.

Mapping · Governance · IAM · IGA
01
Mapping and governance model
  • Mapping of the AI agents used across the organization, including official, experimental and undeclared agents
  • Definition of a governance model specifying the owner, scope, risk level and purpose of each agent
02
Lifecycle rules and alignment
  • Implementation of rules for the creation, validation, modification and decommissioning of identities associated with AI agents
  • Alignment of AI agent governance with existing IAM, IGA, PAM, cloud security and machine identity policies

We secure the access granted to AI agents in order to limit excessive privileges and the risk of unauthorized actions.

Least privilege · Permissions · Zero Trust
01
Access models and granular permissions
  • Definition of access models based on the principle of least privilege, context, agent role and the criticality of actions
  • Implementation of granular permissions across applications, APIs, data, internal tools and cloud environments
02
Control of sensitive actions
  • Framing of sensitive actions through human approval, just-in-time access and conditional rules
  • Reduction of the risk of privilege escalation, lateral movement or indirect access through agent chains

We protect the secrets used by AI agents to prevent leaks and invisible dependencies on credentials.

API Keys · OAuth · Rotation · Vaulting
01
Vaulting and rotation policies
  • Secure vaulting of API keys, OAuth tokens, certificates, service accounts and secrets used by AI agents
  • Implementation of rotation, expiration, revocation and scope-limitation policies for credentials
02
Elimination of exposed secrets
  • Removal of secrets exposed in prompts, memories, logs, configuration files or code repositories
  • Strict separation of credentials by agent, environment, application, risk level and business use

We enable organizations to know exactly what an AI agent has done, with which privileges and on which data.

Logging · SIEM · Audit · Accountability
01
Logging and correlation
  • Comprehensive logging of the actions performed by AI agents: access, requests, API calls, changes, decisions and executions
  • Correlation of AI events with existing SIEM, SOC, ITSM, PAM, IAM and monitoring platforms
02
Detection and audit evidence
  • Implementation of detective controls for abnormal behavior, destructive actions or unauthorized access
  • Production of audit evidence that traces accountability across the user, the agent, the tool and the action performed

We help our clients secure the chains of autonomous actions executed by AI agents.

Human-in-the-loop · Guardrails · Agentic AI
01
Analysis and guardrails
  • Analysis of agentic workflows to identify decision points, sensitive actions and critical dependencies
  • Implementation of guardrails for high-impact actions: deletion, configuration changes, access to production
02
Tool control and human validation
  • Control of the tools accessible to AI agents in order to limit unnecessary or dangerous capabilities
  • Definition of human-in-the-loop scenarios to enforce human validation before critical operations

We support organizations in establishing a measurable control framework to secure the use of AI agents.

NIST AI RMF · EU AI Act · Audit · Risk
01
Risk assessment and internal policies
  • Assessment of the risks associated with AI agents according to use cases, the data handled, the systems accessed and the level of autonomy
  • Definition of internal policies governing the use, access, responsibilities and operational limits of AI agents
02
Dashboards and regulatory alignment
  • Implementation of compliance dashboards covering agents, their privileges, their actions, their exceptions and their incidents
  • Alignment of controls with AI security and risk-management frameworks, notably NIST AI RMF-style approaches

Our numbers talk for us

28

Years of experience

+100

Active Certifications

76

Projects deployed in 2025

17

Countries covered

+40

IAM/PAM/IGA certified experts

Use cases

AI AgentsUniversal bank

AI agent supporting the identity service desk

Automated handling of level-1 access requests under human supervision, with compliance guardrails.

6 months
6,000 requests / month
ITSM · IGA · AI · DORA

The identity service desk was handling a high volume of repetitive requests, leaving the IAM teams little time for higher-value work.

The security leadership wanted to trial an AI agent capable of absorbing level-1 requests, without degrading compliance or replacing human oversight on sensitive decisions.

Value-adding work constantly pushed back by the flow of repetitive tickets
Multi-hour SLAs on trivial requests, a major source of user frustration
ACPR and DORA compliance risk tied to automation without a governed framework
Need to preserve human oversight on sensitive decisions and cryptographic governance
01Mapping of automatable level-1 requests and definition of compliance guardrails
02Design of an AI agent with automatic escalation to a human operator for sensitive or ambiguous cases
03Integration into the existing ITSM with full logging of every decision for audit purposes
04Implementation of a documented AI governance framework aligned with DORA and ACPR requirements
05Pilot phase on 20% of requests with progressive validation before full rollout
65% of level-1 requests handled automatically without human intervention
Average SLA reduced from 4 hours to 15 minutes on automated requests
100% of decisions auditable with full traceability of the accountability chain
IAM teams freed up to focus on high-value projects
AI governance framework validated by the compliance teams and presented to the regulator
65%
Level-1 requests automated
4h → 15min
Average user SLA
100%
Auditable decisions

Another use case, another challenge.

AI AgentsInsurance – mutual group

AI agent supporting access review decisions

Contextual recommendations and risk scoring to turn IGA campaigns into qualitative reviews.

5 months
9,000 employees · 250 applications
IGA · AI · Scoring · Compliance

Access review campaigns suffered from mass, undifferentiated rubber-stamping. Managers, faced with hundreds of accesses to validate, were approving without any real analysis.

The goal was to bring meaning back to the reviews without adding to the managers' workload, by leveraging available data to focus their attention on genuinely high-risk access.

Managers with neither the time nor the context to analyze each entitlement individually
Usage logs, entitlement age, peer comparisons — signals available but unexploited
Strong requirement for explainability towards internal control
Not to impose an automatic decision but to help managers decide better
01Development of a scoring engine leveraging usage logs, entitlement age and peer comparisons
02Integration of the recommendations directly into the existing IGA review interface, without changing the tool
03Each recommendation paired with an explanation readable by the manager and traceable for audit
04The manager remains the decision-maker: the agent proposes, the human validates or overrides. Progressive model learning from the decisions validated by managers
45% increase in high-risk entitlements correctly revoked from the very first campaign using the scoring
40% reduction in manager time per review campaign thanks to contextual recommendations
100% of recommendations explainable and traceable, validated by internal control
Campaign completion rate raised from 68% to 94% thanks to the simplified interface
Continuously improving model, with recommendation accuracy rising with each campaign
+ 45%
High-risk entitlements revoked
− 40%
Manager time per review
100%
Explainable recommendations

How Ident1ty works on your project AI

Solution
integrator

We deploy your AI solution from A to Z.

Continuous Support & Managed Services

We maintain and optimize your AI environment.

Success
Plan

A dedicated ISM to support you.

AI Security
Strategy

We secure the identities and access of your AI agents.

Our technology partners

FAQ

AI Agent and Machine Identity Security FAQ

Clear answers on what machine identities and AI agents are, why they are a risk, and how to govern them.

If you are deploying AI agents faster than you can track them, these are the questions to ask now.

What is machine identity security?
Machine identity security is the discipline that protects and governs non-human identities, such as service accounts, API keys, tokens, workloads, and AI agents, that authenticate and act across systems without a human user. It extends identity security beyond people to the machines that now run most operations.
What is a non-human identity (NHI)?
A non-human identity is any credential that lets a machine authenticate to infrastructure, including API keys, OAuth tokens, SSH keys, workload certificates, service accounts, and AI agents. NHIs now vastly outnumber human identities in most enterprises.
Why are machine identities harder to secure than human ones?
Machines cannot perform MFA, they operate 24/7 with no normal behaviour pattern to baseline against, and they often persist indefinitely with no owner or lifecycle. Traditional IAM was built for human onboarding and offboarding, not high-velocity machine traffic.
What is AI agent security?
AI agent security governs the identity and access of autonomous AI systems. Unlike static service accounts, AI agents acquire permissions at runtime, call APIs, and chain actions across systems, so they need mapping, least privilege, human oversight, and full traceability of every decision.
How do AI agents differ from traditional machine identities?
A traditional machine identity is static: you can inventory, scope, rotate, and revoke it. An AI agent is dynamic: it can request new permissions and act in sequences no policy anticipated. Securing the first is an inventory problem; securing the second is a governance problem.
How does machine identity security relate to IAM, IGA, and PAM?
It extends them to non-human identities. IAM, IGA, and PAM were built around human users; machine identity security applies the same principles of ownership, least privilege, and lifecycle to service accounts and AI agents, ideally under one unified governance model.

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