AI Risk Management
Identify risks where they actually live, on specific models, datasets, and components. Define treatment plans. Track mitigations. Connect your risk posture to your compliance program in one platform.
Identify and assess AI risks
AI risks don’t look like IT risks. They live on specific models, datasets, and use cases, and need their own vocabulary: bias, hallucination, data quality, prompt injection, model drift, IP leakage. Register each risk on the model, dataset, interface, use case, or action where it originates. Capture domain, severity, likelihood, and treatment strategy as structured metadata. Score initial and residual risk separately, and filter your portfolio by any dimension.
From risk identified to risk resolved
Identifying a risk is the beginning, not the end. AI Sigil lets you define one or more mitigations per risk, each with its own owner, status, and timeline. Track the full lifecycle: identification, assessment, treatment plan, implementation, residual risk reassessment. When leadership asks “what are we doing about this risk?” the answer is structured and current, not a promise in a meeting note.
Risk management and compliance in one platform
Your risk register and your compliance controls aren’t separate programs. Risks link to AI systems. AI systems carry frameworks. Frameworks have controls. Controls have assessments. In AI Sigil, identifying a risk on a model and assessing the control that governs it happen in the same environment, on the same data. No exports, no cross-referencing, no reconciliation between tools.
FAQs
What types of AI risks can I register?
Any risk related to an AI system or its components: bias and fairness risks, data quality issues, security vulnerabilities, performance degradation, regulatory non-compliance, ethical concerns, operational failures, and more. Each risk is categorized by domain, severity, and strategy.
Can I link a risk to a specific model or dataset?
Yes. Risks are not limited to the AI system level. You can link a risk to any sub-component: a specific model, dataset, interface, use case, or action. This lets you track risks at the granularity where they actually originate.
How does AI Sigil compute risk scores?
Risk dimensions (likelihood, impact, severity) are captured as structured answers on the risk entity. Computed functions aggregate these into a global severity score. The scoring reflects both initial risk (before mitigations) and residual risk (after mitigations).
What is the relationship between risks and mitigations?
Each risk can have one or more mitigations linked to it. Mitigations are separate entities with their own status, owner, and metadata. The relationship is one-to-many: multiple mitigations can address a single risk, and the platform tracks how many are complete.
Can I track residual risk after mitigations are applied?
Yes. Each risk carries both an initial risk level and a residual risk level. As mitigations are implemented and their status updated, you reassess the residual risk level to reflect the current posture.
How do risks connect to compliance controls?
Risks and controls both link to AI systems through the entity graph. A risk on an AI system is visible alongside the controls governing that system. A completed control assessment provides evidence that associated risks are being managed. The platform surfaces both views: risks per system and controls per system, in one environment.
Can I see all risks across my entire AI portfolio?
Yes. The portfolio view aggregates risks across all AI systems. Filter by severity, mitigation status, risk domain, or specific system. See which systems carry the most risk, which risks are fully mitigated, and where gaps remain.
Who should be responsible for managing AI risks?
AI Sigil assigns an owner to each risk and each mitigation. Typically, the risk owner is the person accountable for monitoring the risk, while mitigation owners handle specific treatments. Both are tracked in the platform with full audit history.
Does AI Sigil support regulatory risk management requirements?
Yes. Regulations increasingly require organizations to implement risk management systems for AI. AI Sigil’s risk registry, mitigation tracking, and audit trail directly support these requirements. When a risk management control is assessed, the evidence links back to the risks and mitigations you’ve documented.
Can I export risk data for board reporting?
Risk data (entities, scores, mitigation status, portfolio aggregates) is accessible via API for integration with board reporting tools or presentation templates. The portfolio view also provides at-a-glance metrics suitable for executive dashboards.