Defensible AI Governance for the Modern Enterprise
Build a robust AI governance strategy. Kovrr provides the structure and solutions GRC leaders need to gain visibility into GenAI and AI risk exposure, and translate findings into business language that guides oversight and investment decisions. The result is a strategic roadmap that withstands scrutiny and positions AI as an issue stakeholders can evaluate and address with confidence.
Benchmark safeguard maturity against frameworks like NIST AI RMF and ISO/IEC 42001.
Identify exposures, including shadow AI and weak governance practices.
Define target states that align oversight with enterprise goals.
Assign accountability and track progress across business units.
Model potential loss events using simulations tailored to the organization’s profile.
Forecast annualized and extreme losses with loss exceedance curves.
Explore exposure across vectors, event types, and damage categories.
Deliver financial metrics boards can rely on when prioritizing investments.
Evaluate Your Cyber Risk and Elevate Your Security Strategy
Cyber risk is a business risk, and without a structured, repeatable way to measure safeguards, organizations will struggle to keep mitigation strategies aligned with evolving threats and stakeholder expectations. Kovrr’s Cyber Control Assessment benchmarks posture against leading frameworks such as NIST CSF, ISO 27001, and CIS, uncovering gaps and guiding smarter investments to elevate security posture.
Building Resilience in the Era of AI Risk
The Rising Stakes of Gen AI and AI Adoption
GenAI and AI system usage is rapidly spreading across the market. While these new tools create immense opportunities, they also introduce new risks that current governance practices struggle to match. At the same time, regulations such as the EU AI Act are emerging, increasing oversight of how AI is used at the business level and adding compliance pressure. By running an AI risk control assessment and quantifying the outcomes, organizations can begin building visibility into risk posture, closing AI governance gaps, and minimizing their exposure.
How Kovrr’s AI Risk Assessment Strengthens Oversight
Kovrr’s AI risk assessment highlights governance and control maturity disparities, equipping teams with visibility and data-driven insight to help reduce exposure, guide oversight efforts, and build resilience.
Evaluate current AI governance maturity across keyprogram areas
Identify AI readiness gaps that could increase exposure
Demonstrate alignment with AI risk frameworks to support compliance
Inform next steps toward stronger oversight and reduced risk
Measurable
Business Impact
After completing the AI Risk Assessment, organizations can conduct a deeper analysis to quantify maturity gaps and assess their potential financial impact on exposure. This more advanced stage translates scores into tangible terms, providing a clear, data-backed foundation for prioritizing mitigation efforts, allocating resources effectively, and, ultimately, strengthening resilience against GenAI threats.
AI Risk Quantification in Practice
Kovrr’s proprietary AI risk quantification process starts by capturing visibility into how GenAI and other AI systems are deployed across your business. It incorporates real-world AI threat intelligence and existing safeguards to simulate realistic loss scenarios. These data-backed scenarios are then used to calculate risk frequencies and severities, giving teams a dynamic view of their risk landscape and strengthening AI governance with a foundation for continuous management.
The Value of Quantifying AI Risk
Quantification turns risk exposure into a decision-making asset that strengthens AI governance and supports strategy, compliance, and investment planning.
Communicate AI Risk to Leadership: Express risks in financial and operational terms that leaders understand, driving informed decisions.
Prioritize and Prove ROI: Allocate resources to high-impact mitigations and show measurable improvement over time.
Strengthen GRC Programs: Use quantified results to guide capital allocation, set risk appetite, and track materiality.
Inputs such as industry, revenue, AI models in use, and key regulatory obligations set the baseline for risk analysis.
Capture model access, data types handled, reliance factors, and existing controls to shape accurate, customized risk profiles.
Our AI risk quantification engine applies AI-specific threat intelligence to calculate incident frequency and severity.
Get clear metrics like Annualized Loss Expectancy, loss exceedance curves, and breakdowns by access vector, event type, and damage type.
Explore the controls with the highest potential to reduce AI risk exposure and allocate resources based on their quantified impact.
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AI Governance and Risk Management FAQs
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