AI in risk management: Practical applications and considerations
GRC teams are using AI to replace fragmented risk tools and generate live reports, while 59% still rely on spreadsheets or nothing, the report found.
- Posted Tuesday, Vanta reported in the 2026 State of GRC report that 59% of governance, risk, and compliance practitioners rely on manual spreadsheets, making risk management slow and point-in-time.
- Tool sprawl and fragmented data across disconnected systems force teams to manage vendors, controls, and incidents in isolation, causing manual reconciliation while critical signals get lost in alert fatigue.
- Embedding AI across the full risk life cycle connects fragmented identification, scoring, mitigation, and reporting into a unified system, generating live, actionable risk reports on demand.
- AI-Enabled platforms must prioritize explainability to avoid "black box decisions," as hidden logic or bias can unintentionally skew risk assessments, creating governance challenges for organizations.
- Risk teams should focus on structured programs and stress-tested processes before scaling AI, explained Jill Henriques, Vanta's go-to-market GRC subject matter expert, noting the goal is to "support enterprise risk management within the organization.
11 Articles
11 Articles
AI in risk management: Practical applications and considerations - The Mexico Ledger
AI in risk management: Practical applications and considerationsRisk data is expanding faster than teams can manually structure, score, and act on it. As organizations scale, traditional risk management processes built around scattered artifacts become difficult to sustain. Artificial intelligence is helping many teams address these challenges—particularly, turning fragmented systems into a more continuous, data-driven risk management program.T…
AI in risk management: Practical applications and considerations - Seward Independent
AI in risk management: Practical applications and considerationsRisk data is expanding faster than teams can manually structure, score, and act on it. As organizations scale, traditional risk management processes built around scattered artifacts become difficult to sustain. Artificial intelligence is helping many teams address these challenges—particularly, turning fragmented systems into a more continuous, data-driven risk management program.T…
thepampanews.com
AI in risk management: Practical applications and considerations
Vanta reports that AI enhances risk management by integrating fragmented processes into a cohesive, data-driven program, improving efficiency and reducing human error.
AI in risk management: Practical applications and considerations - Stateline Publications
AI in risk management: Practical applications and considerationsRisk data is expanding faster than teams can manually structure, score, and act on it. As organizations scale, traditional risk management processes built around scattered artifacts become difficult to sustain. Artificial intelligence is helping many teams address these challenges—particularly, turning fragmented systems into a more continuous, data-driven risk management program.T…
AI in risk management: Practical applications and considerations - Hillsboro Sentry Enterprise
AI in risk management: Practical applications and considerationsRisk data is expanding faster than teams can manually structure, score, and act on it. As organizations scale, traditional risk management processes built around scattered artifacts become difficult to sustain. Artificial intelligence is helping many teams address these challenges—particularly, turning fragmented systems into a more continuous, data-driven risk management program.T…
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