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“Security is everyone’s job, but identity is the foundation. In an AI-driven world, that foundation must extend to machines.”
— Werner Vogels, CTO, Amazon Web Services
As AI systems move from responding to prompts to acting autonomously, identity becomes the control plane that determines whether AI can be trusted at all. When machines can deploy infrastructure, access sensitive data, and trigger multi‑step workflows without human intervention, the question is no longer what AI can do, it’s who (or what) is authorised to act, and under what constraints.
In an agentic AI world, even AI needs a login.
Over the past few years, enterprises have embraced generative AI - systems that respond to prompts, summarise information, and assist human decision‑making. But agentic AI represents a fundamental shift.
Agentic systems don’t just generate outputs; they act.
They can:
In effect, these systems behave like digital employees, operating continuously, at machine speed, and often across multiple environments.
This changes the identity question from: “Who is logging in?”
To: “Who is acting in the system, what authority do they have, and can we prove it?”
When AI systems act independently, failures are no longer limited to incorrect outputs. They become operational, financial, regulatory, and reputational risks.
Traditional Identity and Access Management (IAM) models were designed around human users:
Agentic AI breaks all these assumptions.
Modern enterprises now operate with a rapidly expanding set of non‑human actors:
In this environment, identity is no longer just a gatekeeper. It becomes the governance layer that enforces accountability,trust, and control.
To safely operate agentic AI, enterprises require:
Without these foundations, autonomous AI scales risk faster than organisations can see or respond to.
In 2024, Toyota disclosed that hard‑coded machine credentials had been exposed via public GitHub repositories.
The result:
A non‑human account with excessive privileges exposed customer telematics and internal systems. The credentials remained active and undetected for more than five years.
The IAM lesson:
If you can’t inventory machine identities, you can’t authenticate them. And if you can’t authenticate them, you can’t trust their actions.
This incident underscores a broader problem: many enterprises govern human identities rigorously while allowing machine and service identities to operate with little oversight.
In 2025, attackers compromised the Nx build system by injecting malicious code into widely used packages distributed through the developer supply chain.
The result:
Compromised tooling silently harvested GitHub tokens, cloud credentials, SSH keys, and AI tool tokens from thousands of developer environments. These non‑human credentials were then published to public repositories, enabling large‑scale secondary abuse.
Because the stolen identities belonged to tools, pipelines, and AI workflows, and not named users, the attack bypassed traditional, user‑centric security controls entirely.
The IAM lesson:
If you can’t secure identities inside your supply chain, you can’t trust your build systems and therefore can’t trust the software they produce.
These incidents are not edge cases. They are early warning signals of what happens when autonomous systems inherit credentials without identity‑first governance.
Passwords, static API keys, and long‑lived tokens were never designed to govern autonomous decision‑makers operating at scale.
As agentic AI begins to act across cloud, hybrid, and enterprise environments,access must become:
This is where modern standards such as OAuth 2.1, OIDC, and emerging protocols like GNAP and verifiable credentials become critical.
OAuth gives humans structured access to systems. GNAP, DIDs, and workload identity frameworks extend those same principles to machines and AI agents, enabling fine‑grained delegation, least privilege, and auditable trust.
The challenge isn’t choosing a standard. It’s recognising that static trust models cannot govern autonomous actors.
As agentic AI proliferates, identity becomes the foundational control plane that ensures autonomous systems operate securely and within policy.
Identity‑first AI governance requires:
This approach mitigates emerging AI‑driven risks, including:
Crucially, identity‑first governance doesn’t slow AI adoption. It’s what enables enterprises to deploy agentic systems confidently, at scale, and under regulatory scrutiny.
The relationship between IAM and agentic AI is bidirectional.
While IAM must evolve to govern AI agents, agentic AI can dramatically improve IAM itself:
Agentic AI shifts IAM from reactive enforcement to proactive, intelligent orchestration, capable of governing thousands of human and non‑human identities with precision and accountability.
Across the evolution from human users to machines and autonomous agents, one principle remains constant: trust must be enforced, not assumed.
In a world where AI acts at machine speed across entire enterprise ecosystems, identity determines whether those actions can be trusted, audited, and governed.
Identity is no longer just about logging in. It’s the control plane, audit layer, and trust fabric for a future where humans, machines, and autonomous AI coexist.
If AI is making decisions, identity frameworks decide whether those decisions are safe.
Even AI needs a login.
Most organisations understand why identity matters for agentic AI, the harder challenge is making it work in complex, real-world environments.
Colibri helps enterprises move from theory to execution by embedding identity-first principles directly into AI, cloud, and data platforms.
Our approach focuses on three outcomes:
We help organisations design identity architectures where human, machine, and AI agent identities are governed consistently across cloud, data, and application layers, rather than treated as isolated security controls.
Colibri works with platform, security, and data teams to ensure agentic AI systems are deployed with per-agent identity, least-privilege access, automated credential lifecycle management, and full auditability so autonomy doesn’t come at the cost of trust.
By integrating modern IAM patterns (OAuth, workload identity, continuous authorisation) with cloud-native and data platforms, we help organisations scale agentic AI safely without slowing innovation or overburdening teams.
The result is not just more secure AI, but AI that can be trusted, governed, and operated at enterprise scale, even as autonomy increases.
If you’re exploring or scaling agentic AI and want to ensure identity, access, and governance are built in from day one, Colibri can help.
We work with security, cloud, and data leaders to design and implement identity-first architectures that make autonomous AI safe, auditable, and production-ready.
Talk to Colibri about identity-first agentic AI.
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