What is Governance for AI and AI Agents?
Source: Dev.to
AI governance has recently gained traction because enterprises need safe AI systems for real‑world applications. Yet many still wonder what governance actually entails for AI agents.
In this article we’ll:
- unpack what AI governance really means,
- explain why getting it right is a complex problem, and
- show how AI orchestration platforms like Credal can help teams simplify their governance approach.
What Is AI Governance?
At its core, AI Governance is a collection of policies, processes, and controls that guide how AI systems (models, applications, and agents) should be built, rolled out, and operated in a safe and compliant manner. These frameworks ensure AI is introduced and scaled responsibly—without security vulnerabilities, compliance violations, or reputational harm.
In theory it’s straightforward; in practice, it’s not.
AI Governance focuses on a handful of sub‑problems that only surfaced with the rapid rise of AI agents. Because AI governance is still relatively new, existing frameworks (e.g., SOC 2) address it only partially and mainly in relation to data.
Why AI Agents Introduce New Risks
Developers and users have embraced AI agents quickly. Their autonomy and high customizability make them attractive, but those same qualities introduce new security and risk challenges.
Two Main Risk Categories
| Risk Type | Description |
|---|---|
| Data Risk | Agents may expose sensitive information to employees who lack appropriate authorization, leading to regulatory penalties or jeopardized contracts. |
| Mutation Risk | Agents often have write access, so they could incorrectly update external systems (e.g., send an email, post an unauthorized Slack message, delete a ticket, or make a payment). |
Mitigating these risks requires a governance framework that clearly defines and enforces how access is provisioned across an agent ecosystem.
Who Is Responsible?
Responsibility for implementing these principles rests with the customer, not the vendor. Vendors are generally unwilling to assume liability for mistakes made by their applications—or their agents. Because agent behavior is largely unpredictable, enterprises must own the safeguards.
Example: Many vendors offer agents that can send emails or create Jira tickets. None will cover legal fees if an agent accidentally leaks sensitive data to a public board or sends PII to the wrong recipient.
Consequently, enterprises need the right tooling to manage agent risks, especially regulated companies facing significant penalties for data exposure. This demand has spawned a market of third‑party solutions (e.g., Credal) positioned between vendors and enterprises.
The Three Core Tenets of AI Governance
-
Access
- Agents must receive permissions that do not bypass controls applied to humans, servers, or devices.
- Each agent should have a designated owner and inherit the same (or fewer) permissions as that owner, following the principle of least privilege.
-
Auditing
- Agent activity must be tracked so errors or breaches can be investigated and reproduced.
- Unlike humans—where you might simply ask “Who deleted this table?”—agents require deterministic monitoring to maintain a useful history.
-
Human‑in‑the‑Loop (HITL)
- For critical operations, a human should explicitly approve the agent’s access after reviewing a concise summary of the intended action.
- This reduces the risk of catastrophic mistakes (e.g., a full database drop).
Below we focus on the Human‑in‑the‑Loop tenet, because the risks and controls vary depending on the specific type of action being performed.
Determining Which Actions an Agent May Execute
Every action carries a different degree of risk. We can classify actions into three categories:
| Category | Risk Level | Typical Treatment |
|---|---|---|
| Read‑only | Lowest | Human owner grants access within their own scope of permissions. |
| Low‑risk write | Low | Agents may proceed without waiting for human approval, provided permissions and auditing are properly set. |
| High‑risk write | High | Enterprises should mandate explicit human approval. |
How to Manage Each Category
-
Read‑only actions – The human owner is responsible. Through a governance framework, the owner must grant the agent access within their own scope of permissions.
-
Low‑risk write actions – Agents can proceed autonomously. Requiring human approval for every action would be more obstructive than beneficial, as long as permissions and auditing are correctly configured.
-
High‑risk write actions – Enterprises should require explicit human approval. The boundary between low‑risk and high‑risk actions is organization‑specific. For example:
- Updating a Salesforce record → Low risk
- Sending a payment → High risk
In high‑risk scenarios, the human owner providing approval assumes accountability. In low‑risk scenarios, responsibility rests with the agent’s owner and the underlying governance controls.
Bottom Line
AI governance is essential for safely deploying AI agents at scale. By establishing clear access, auditing, and human‑in‑the‑loop controls—and by classifying actions by risk—organizations can mitigate data and mutation risks while still reaping the productivity benefits of autonomous AI agents.
Platforms like Credal can help operationalize these principles, giving enterprises the tooling they need to govern AI agents responsibly. Centralized agent governance becomes crucial in larger or regulated enterprises; codifying practices such as defining high‑ and low‑risk actions helps demonstrate defensibility to regulators.
Credal is an AI governance and orchestration platform with ready‑to‑use managed agents, built‑in auditing, human‑in‑the‑loop, and permissions inheritance. It sets the environment and rules for agents without dictating low‑risk versus high‑risk actions or human‑in‑the‑loop workflows—those decisions remain with the enterprise.
If you are interested in learning more about Credal, sign up for a demo today.