AI security and risk

Artificial intelligence introduces operational and security considerations that traditional software stacks do not encounter. Beyond standard network and application vulnerabilities, enterprise AI deployments must account for non-deterministic model reasoning, direct and indirect prompt injection, system prompt leakage, and autonomous agent execution.

Rival Enterprise addresses these risks through an external, model-independent governance model that enforces strict security boundaries around every execution path.

Verify before deployment

This page outlines the enterprise AI security architecture for Rival Enterprise. Specific security controls, threat mitigations, and compliance parameters should be reviewed and verified by your internal security and risk teams prior to final deployment.

AI-specific threats and mitigations

Deploying AI at scale introduces novel attack vectors that require specialized runtime controls.

• Direct and indirect prompt injection — Attackers attempt to override system instructions or inject malicious instructions through untrusted external documents, emails, or API payloads.
  • Rival mitigation: Input validation guardrails, prompt boundary isolation, and deterministic runtime checks treat model logic as untrusted, preventing injected prompts from altering system policies.
• System prompt and data leakage — Unintended disclosure of system instructions, internal data structures, or sensitive context windows.
  • Rival mitigation: Agents reach data only through admin-approved connectors and tools, and data residency keeps tools inside approved regions. Rival does not mask or redact data inside a run.
• Excessive agency and unintended execution — Autonomous agents taking unauthorized actions or escalating system privileges beyond their intended scope.
  • Rival mitigation: An agent’s needs approval guardrails pause high-consequence actions, such as sending emails or changing records, until the person using it approves; blocked actions are refused outright.

Data governance and privacy controls

Data security in AI hinges on three questions: where does data flow, who can access it, and how is it used?

• Controlled integrations — Agents reach internal functions strictly through admin-approved connectors with scoped, token-based permissions.
• Allowed model routing — Sensitive enterprise workloads run exclusively on whitelisted foundation models operating under strict zero-data-retention and zero-training policies.
• Context lineage and traceability — Immutable audit logs capture every prompt input, context document, retrieved chunk, and generated output.
• Zero model training — Customer inputs, agent context, and execution outputs are never used to train public foundation models or shared third-party algorithms.

Least-privilege identity and access

Traditional access control models assume predictable human request-response cycles. AI agents execute multi-step tasks autonomously, which makes least-privilege enforcement critical.

• Scoped connector grants — Connectors are granted to specific agents or roles rather than opened broadly across the workspace. An over-privileged agent cannot execute actions outside its granted system connectors.
• Task-bound permissions — Agents inherit only the explicit permissions needed for their immediate workflow.
• Immediate access removal — Suspending or removing a member stops their runs at once. (Single sign-on through your own identity provider is not available yet.)

Third-party and foundation model risk

Rival’s model-agnostic framework provides the administrative levers needed to manage third-party supply chain risks.

• Centralized whitelisting — Admins control exactly which third-party or private foundation models are approved for organizational use.
• Runtime isolation — Execution sandboxes isolate code execution and API tools, preventing third-party model outputs from directly altering system parameters.
• Audit transparency — Detailed activity logs record model calls, token usage, and response payloads, ensuring complete visibility across external dependencies.

A quick example: a governed enterprise context

Consider a security team evaluating an AI assistant designed to process customer contracts.

1. The risk question — Can an attacker use indirect prompt injection in a customer contract to exfiltrate private internal documents?
2. Platform enforcement — The workflow runs on an admin-approved model bound by global guardrails. The contract text is sanitized on ingestion, document retrieval is scoped strictly to the user’s role permissions, and any external data transmission requires explicit human sign-off.
3. Verifiable safety — The security review transitions from trusting model behavior to verifying a deterministic, multi-layered security model.

Framework summary

Threat domain	Primary risk vector	Rival platform mitigation
Prompt security	Direct and indirect prompt injection	Deterministic input validation and boundary segregation
Data privacy	System prompt and data leakage	Approved connectors and tools, data residency, and zero-training models
Execution agency	Unintended or unapproved actions	Human-in-the-loop approval gates and mandatory action checkpoints
Access boundaries	Excessive agent privilege	Scoped connector grants and role-based asset assignments
Third-party risk	Model supply chain exposure	Whitelisted model routing, runtime isolation, and immutable execution audit logs

Next steps

Security architecture Review platform infrastructure protections, encryption, and tenant isolation.

Approval workflows Configure the human sign-off checkpoints that gate autonomous action.

Guardrails and allowed models Enforce organization-wide model rules and data privacy policies.

Connector governance Scope which systems agents can reach, and under whose authority.