> For the complete documentation index, see [llms.txt](https://docs.sprinto.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.sprinto.com/data-library/ai-systems/dashboard-actions/assess-ai-system-risk.md). # Assess AI System Risk The AI Systems module in Sprinto includes AI-specific risk assessment workflows that help organisations evaluate the governance, operational, and compliance risks associated with AI systems. Risk assessments help organisations: * Evaluate AI-related risk exposure * Classify AI systems by risk level * Maintain governance records * Support AI compliance workflows * Prioritise reviews and remediation * Maintain periodic risk reviews Sprinto supports configurable AI risk scoring workflows aligned with evolving AI governance requirements. *** ## Navigate to the Risk Score Tab To access AI system risk scoring: 1. Log in to the Sprinto dashboard. 2. Navigate to **Data Library**. 3. Select **AI Systems**.
3. Open an AI system from the **Added AI Systems** tab. 4. Select the **Risk score** tab.
The Risk score tab displays all configured AI risk factors and associated scoring values. *** ## How AI Risk Scoring Works Sprinto evaluates AI system risk using configurable risk factors. Each risk factor contains: * Multiple scoring options * Configurable score values * Risk contribution logic As users select values for risk factors, Sprinto automatically: * Calculates cumulative risk scores * Assigns risk levels * Updates governance posture across the AI inventory *** ## AI Risk Factors Risk factors help organisations evaluate the impact and governance posture of AI systems. Depending on your configuration, risk factors may evaluate: * Operational scale * Deployment exposure * Data sensitivity * Decision reversibility * Affected user groups * AI output usage * Business criticality * Regulatory impact * Safety-related usage Risk scoring workflows can be customised from the Configuration tab. *** ## Add Risk Factor Values To assess AI system risk: 1. Open the AI system. 2. Navigate to the **Risk score** tab. 3. Click **Add value** beside a risk factor.
This opens the risk scoring drawer. *** ## Score AI Risk Factors Inside the risk scoring drawer: 1. Expand each risk factor section. 2. Review the available scoring values. 3. Select the appropriate value. 4. Repeat the process for all mandatory risk factors.
Sprinto displays scoring values as selectable options for each factor. *** ## Mandatory Risk Factors Some risk factors may be marked as mandatory. The **Save changes** button remains disabled until: * All mandatory risk factors are scored * Required selections are completed This helps organisations maintain consistent risk assessments. *** ## Save AI Risk Scores After completing the assessment: 1. Click **Save changes**.
Sprinto: * Saves the selected values * Calculates cumulative risk scores * Updates the AI system risk posture * Displays updated scores in the Risk score tab *** ## Review Risk Scores After saving, the Risk score tab displays: * Risk factor values * Assigned scores * Calculated risk levels This helps organisations review how individual risk factors contribute to the overall AI risk posture. *** ## Edit Risk Scores To update existing risk scores: 1. Navigate to the **Risk score** tab. 2. Click the edit icon beside the risk factor value.
3. Update the required scoring values. 4. Click **Save changes**.
Sprinto recalculates the AI system risk score automatically. *** ## AI Risk Levels Sprinto supports configurable AI risk levels. Depending on your configuration, risk levels may include: * No risk * Low risk * Medium risk * High risk * Critical risk Risk levels are automatically assigned based on configured score thresholds. *** ## Risk Scoring Configuration AI risk scoring workflows are configurable from the Configuration tab. Administrators can customise: * Risk factors * Risk factor values * Score thresholds * Mandatory scoring requirements * Risk level ranges This helps organisations align AI governance workflows with internal risk policies. *** ## Example AI Risk Dimensions AI risk assessments may include factors such as: ### Data Privacy Evaluates how AI systems handle sensitive or personal data. Examples include: * PII handling * PHI exposure * Data retention practices * Training data usage *** ### Operational Scale Evaluates the deployment scale of the AI system. Examples include: * Internal team usage * Organisation-wide deployment * Public-facing deployment * Population-scale usage *** ### Business Criticality Evaluates the operational importance of the AI system. Examples include: * Customer-facing workflows * Revenue-impacting systems * Internal automation tools * Low-impact support systems *** ### Decision Impact Evaluates how AI outputs influence users or business decisions. Examples include: * Human-reviewed recommendations * Automated decisions * Access control decisions * Safety-related outcomes *** ## AI Governance Best Practices When assessing AI system risk: * Review risk assessments periodically * Include compliance and security stakeholders * Evaluate sensitive data exposure carefully * Document business-critical AI usage * Reassess risk after major system changes * Align scoring criteria with governance requirements * Maintain evidence for audit reviews *** ## Relationship Between Risk and Due Diligence Risk scoring helps organisations determine: * Whether additional due diligence is required * Which AI systems require governance reviews * Which systems need periodic reassessment * Which systems may require remediation activities Higher-risk AI systems may require: * Additional document collection * Security questionnaires * Governance reviews * Management approvals *** ## Monitoring AI Risk Workflows Sprinto supports governance monitoring workflows related to AI risk. Examples include: * AI system risk should be scored * Periodic risk assessment reviews * Management review workflows * Risk reassessment monitoring These workflows help organisations maintain ongoing AI governance oversight. *** ## Related Information * [AI Systems Overview](/data-library/ai-systems.md) * [How AI Systems Work](/data-library/ai-systems/how-ai-systems-work.md) * [Add and Manage AI Systems](/data-library/ai-systems/dashboard-actions/add-and-manage-ai-systems.md) * [Perform AI System Due Diligence](/data-library/ai-systems/dashboard-actions/perform-ai-system-due-diligence.md) * [Configure AI Systems](/data-library/ai-systems/dashboard-actions/configure-ai-systems.md) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.sprinto.com/data-library/ai-systems/dashboard-actions/assess-ai-system-risk.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.