In Identity and Access Management (IAM), most organizations begin by defining a schema: name, employee ID, manager, department, title, and maybe a few computed fields like employmentDuration or contractRemainingDays or isManager or riskScore. This works for provisioning accounts and enforcing basic access policies.
But the reality is different: identity is not static. A user’s access, behavior, and risk profile evolve constantly. The schema you start with cannot anticipate everything that will matter later. That’s why identity metadata must be dynamic — enriched at runtime by AI Agents that detect, generate, and retire attributes as needed. This creates a living identity, sometimes called the digital DNA of the user.
A static schema captures the basics of a person’s role. But many governance-critical signals emerge only after someone is active in the environment.
Take the case of a contractor: on day one, their schema includes employmentType = contractor. Six months later, AI notices they still hold database admin rights long after the project ended. At runtime, the system introduces new attributes like:
Once IT removes those rights, the attributes disappear. That’s the point: dynamic attributes are ephemeral. They exist only as long as the condition holds, preventing the schema from bloating with obsolete fields.
AI Agents continuously monitor events, context, and policy. They cultivate new metadata when it becomes relevant, and retire it once remediated. This enrichment happens across several categories:
When a developer starts logging in at 2 AM, AI introduces afterHoursActivitySpike = true. Once their pattern normalizes, the flag vanishes.
A finance associate accumulates privileges usually reserved for directors. AI generates peerDeviationFactor = high. If access is right-sized, the attribute goes away.
A Singapore-based employee logs into an EU payroll system. AI surfaces geoPolicyViolationCount = 1. After corrective controls, the counter resets.
An employee under HR performance review shows unusual downloads. AI infers likelyResignation = 68%. Once the situation resolves, the prediction no longer applies.
Sometimes risks aren’t about entitlements, but how governance tasks are handled.
These attributes exist only for individuals exhibiting the behavior — and they disappear once patterns change.
Dynamic attributes can surface at every stage:
Each attribute emerges contextually, remains as long as needed, and vanishes once remediated.
Static schemas create blind spots. Dynamic metadata ensures IAM reflects current reality, not just design intent. Whether it’s a startup catching interns with leftover repo access, a mid-size firm exposing shared contractor accounts, or a global bank managing cross-border compliance risk — treating identity as living strengthens governance everywhere.
Identity is not just who someone is on paper. It is the sum of their changing behaviors, privileges, and context. AI Agents cultivate this digital DNA, generating and retiring metadata dynamically, so IAM systems stay aligned with reality.
The future of governance lies not in building the perfect schema once, but in embracing identity as living and dynamic.