Enterprise AI security

AI introduces security considerations that traditional software doesn’t. This page covers the ones specific to running AI across an enterprise — the questions a modern security review will raise. [VERIFY: confirm this section with Rival’s security team before publishing.]

AI-specific risks

AI systems can be prompted to behave in unintended ways, can surface data they shouldn’t, and can take actions with real consequences. The mitigations run throughout Rival: guardrails bound behavior, approvals keep humans on consequential actions, and connectors limit what any agent can reach. Security here isn’t one feature — it’s the governance model doing its job. See Governance & Approvals.

Data governance

The central AI-security question is: where does our data go, and what is it used for? That means controlling which systems agents can reach (via approved connectors), which models sensitive work can run on (via allowed models), and keeping a record of it all (via audit logs). Data governance is how you keep AI useful without it becoming a leak.

Access controls

The same least-privilege discipline that protects any system protects AI: give each person and each agent the minimum access needed. In an AI context this matters more, because an over-privileged agent can act on that access autonomously. See Roles & Permissions.

Third-party and model risk

Using external models and tools introduces third-party risk. Managing it means knowing what’s in use, controlling what those components can access, and being deliberate about which are approved. Rival’s model-agnostic, approval-based design is what gives you those levers.

A quick example

Before rollout, a security team asks: can an agent send customer data to an outside model? The answer is governed — sensitive work is restricted to approved models, agents can only reach approved connectors, and every action is logged. The risk isn’t “trust the AI”; it’s a set of controls you set and can verify.