When agents act on their own, governance has to live in the data layer

EDB Postgres artificial intellect is an open, enterprise-grade sovereign information and artificial intellect platform that unifies transactional, analytical, and artificial intellect workloads — with governance enforced where the information lives. That is a reason to be deliberate about where control lives, not a reason to slow down. That responsibility can’t be met in hindsight or with a set of abstract policies that live on paper but not in practice. Governance that depends on reviewing an action before it happens cannot keep pace with a system that acts in milliseconds, across many systems at once.Governance has to become executable, and enforced where agents actually do their work: at the operational information layer, in the context, and exactly at the moment it is happening. We are asking agents to do intelligent things; that requires intelligent rules.The instinct is to add guardrails around the agent: instructions, policies, and monitoring layered above the model. Wherever you are in your artificial intellect adoption journey, enforcement at the information layer is what lets you move faster rather than slower. That is the difference between hoping an actor stays in bounds and constructing bounds it cannot cross to begin with.The controls that make this real are ones many enterprises already run at the information layer: role- and attribute-based access, row- and column-level security, classification and masking, policy as code, and complete audit trails. Additionally, a principle that says artificial intellect must be auditable is meaningful only if the organization can reconstruct what the agent did, what information it touched, which user it acted for, and what resulted. When governance lives at the information layer, it holds regardless of how the agent was built or how it behaves, because the control is a property of the database itself, not a promise made by the agent.Agent behavior may be probabilistic. A policy that says an agent should not reach a certain class of information is meaningful only if the system can deny that access at the moment the agent requests it. What changes is that the agent's purpose is part of what it evaluates, and part of what the record proves afterward,” says Priyanka Jain, VP, product management, information & artificial intellect governance, EDB. Controls at the agent layer are only as reliable as the agent’s output is predictable, and autonomy is precisely the property that makes that output hard to predict. Once purpose is bound to identity, the policy engine can evaluate it the same way it evaluates role or department today, and the record of what happened can capture not just who acted and what they touched, but what they declared they were there to do.In practice, this resolves into nine controls, grouped under three imperatives:Enforce itRole- and attribute-based access control enforced at query time, for agents as well as usersDynamic column masking driven by the same policy pathAgent identity as a first-class principal, with declared purpose bound at session start and the acting user preservedSee it and prove itClassification and tagging that drives policySession-level audit logging that records which agent acted, for which user, and under what declared purposeLineage across pipelines, so a result can be traced back to the request that produced itUnify and hardenCentralized, portable policy managementEncryption at rest and in transitConsistent enforcement across on-prem, cloud, and sovereign or air-gapped environments“Declared purpose is what makes the difference. But if you change the context (the car has just crashed, there’s a fire, someone is hurt and needs to get out), then the rule you actually want is the opposite. Governed this way, agents are identified, scoped, monitored, and auditable. The enforcement mechanism does not change. Presented by EDB As enterprises give artificial intellect agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it?These are your agents, running on your models, touching your information in your infrastructure — and the responsibility for what they do sits with you. It becomes an attribute the access layer already understands, evaluated in the same policy path as role and row-level security. The enterprise can adopt them faster, because security, risk, and leadership teams trust the operating model underneath.Open, sovereign, and enforceable at the sourceBuilt on open source Postgres, this open foundation keeps enterprises in control of where their information lives, who can reach it, and under what policy, without ceding governance to a layer they don’t own or can’t inspect. Those mechanisms matter, but they share a structural limit: The car-door rule is plausible right up until the moment you actually have to decide whether to open the door. Governance cannot beThe enterprise should not rely on a model choosing to follow policy. Context in the moment is everything. The difference is that agents now have to pass through them.A digital leash, not a locked doorThe goal is not to stop agents from doing useful work. Identity management has to treat the agent as a principal in its own right, with its own identity and a purpose declared when the session opens. The information layer is the enforcement pointAgents create value by touching information. For the full framework, see EDB’s white paper Governing Agentic artificial intellect at Enterprise Speed.Max Romanenko is Chief Technology Officer at EDB.Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. They query it, retrieve it, transform it, and increasingly act on it. What agents change is not the mechanism, but who the mechanism has to recognize. Followed literally, an agent could never get in or out of the car at all. The controls are already in the database. The enterprises that enforce governance at the information layer can move aggressively on artificial intellect, because the thing protecting their information is more than just wishful thinking. It is to define how far an agent can go, what it can touch, what it can change, what requires escalation, and how the organization can reconstruct events if something goes wrong. For more information, contact sales@venturebeat.com. Agents need rules in the context of the moment, because they don’t exercise overriding judgment of their own actions.Consider a simple rule: Never open the car door. For regulated industries, that combination of information sovereignty and source-level enforcement isn’t a nice-to-have; it’s the precondition for putting agents into production at all.Agentic systems will keep getting more capable and more autonomous. The policy has to be enforced by the system.