AI risk management
A structured approach to identifying, assessing, and mitigating AI-specific risks in enterprise organizations — from data leakage through injection attacks to compliance exposure.
Risk assessment matrix
Data Risk
- • Customer PII in AI prompts
- • Source code / credentials exposure
- • Internal document leakage
Mitigation: AI DLP with PII masking and document detection
Security Risk
- • Prompt injection attacks
- • System prompt extraction
- • Jailbreak and role hijacking
Mitigation: Prompt firewall with ML-based detection
Compliance Risk
- • Missing audit trails
- • Unenforceable AI policies
- • Regulatory non-compliance
Mitigation: Governance framework with automated enforcement
Operational Risk
- • Shadow AI tool proliferation
- • Inconsistent security controls
- • AI vendor dependency
Mitigation: Shadow AI detection and multi-provider governance
Risk management lifecycle
- Identify — Map all AI applications, data flows, and exposure points. Shadow AI detection reveals hidden risks.
- Assess — Score each risk by likelihood and business impact. Threat modeling provides structured assessment.
- Mitigate — Deploy proportional controls: DLP for data risk, prompt firewall for security risk, policy enforcement for compliance risk.
- Monitor — Continuous measurement through audit trails and SOC integration.
- Review — Quarterly reassessment as AI tools, usage patterns, and regulations evolve.
Manage AI risk
Deploy automated risk controls across your AI deployment.
Frequently asked questions
What are the main AI risks for enterprises?+
Key enterprise AI risks include: data leakage through AI prompts (sensitive data reaching third-party providers), prompt injection attacks (adversarial manipulation of AI behavior), compliance violations (unaudited AI interactions), shadow AI (unsanctioned AI tool usage), and reputational risk (AI-generated content issues).
How do I quantify AI risk?+
Quantify through metrics: percentage of AI interactions containing sensitive data, injection attempt rate, shadow AI tool count, unaudited interaction volume, and compliance gap analysis. PromptWall dashboards provide these metrics in real-time.
How does AI risk differ from traditional IT risk?+
AI risk includes unique categories: data exfiltration through conversational AI, model manipulation via prompt injection, regulatory exposure from new AI regulations (EU AI Act), and productivity risks from overly restrictive AI policies. Traditional IT risk frameworks (ISO 27001) must be extended to cover these.