As AI systems integrate into business processes, organizations face growing AI governance, risk, and compliance needs. In our prior research, we tested AI risks in practice with an AI bias benchmark, finding persistent bias around race, gender, and socioeconomic assumptions in several models. These findings underscore the importance of AI GRC tools, which help continuously monitor controls, identify potential risks, and strengthen compliance management.
Explore what AI GRC is and discover top AI GRC software, curated based on our earlier work on AI governance tools and AI risk assessment.
What is AI in GRC?
AI GRC (AI Governance, Risk & Compliance) integrates artificial intelligence into traditional governance frameworks to improve risk management and compliance. It uses AI systems, such as machine learning, natural language processing, and data analytics tools to automate routine compliance tasks and continuous monitoring.
For example, AI GRC tools can automatically update control requirements when regulations change (e.g. per the EU AI Act) and maintain compliance with complex standards.
Core components
Typical core components include:
- AI governance: Establishes frameworks and policies, including data governance and ethics guidelines, to ensure responsible AI adoption.
- For instance, an AI governance committee, along with roles such as a chief risk officer or AI risk officer, oversees AI implementation, evaluates AI models, and monitors AI risk across the organization.
- Explore more on AI governance tools.
- Risk management: Integrates AI into risk management programs to support strategic risk analysis and evaluate risk scenarios.
- AI automates risk assessments and analyzes cyber and operational data, enabling proactive risk management and operational risk management while helping identify potential risks early.
- Compliance management: Compliance management uses AI to automate routine compliance tasks, support compliance monitoring, and track regulatory requirements.
- AI helps compliance teams identify potential compliance risks, maintain compliance, and reduce manual processes while improving the accuracy of compliance documentation.
Key AI technologies in GRC
These artificial intelligence technologies are embedded within organizational operational processes and GRC workflows to support continuous monitoring processes and periodic assessments.
GRC Co-pilots
GRC co-pilots are AI-powered assistants embedded in GRC platforms. They support compliance teams by answering regulatory questions, drafting policies, summarizing compliance documentation, and evaluating control effectiveness. These co-pilots reduce manual effort and improve consistency across GRC processes.
Multi-Agent Systems (MAS)
Multi-agent systems consist of multiple AI agents, each assigned to a specific task such as monitoring regulatory changes, tracking risk indicators, or scanning audit evidence. These agents share insights to support holistic risk identification and faster response to emerging risks.
Large Language Models (LLMs)
LLMs use natural language processing to interpret regulatory texts, policies, contracts, and internal documentation. They help identify gaps between regulatory requirements and existing controls, support compliance monitoring, and assist with predictive analytics related to compliance violations and risk scenarios.
Machine Learning (ML)
ML models analyze historical data to detect patterns, score risks, and forecast future risks. ML is commonly used for risk assessments, anomaly detection, cyber risk management, and trend analysis.
Natural Language Processing (NLP)
NLP focuses on extracting structured insights from unstructured data sources such as regulations, audit reports, emails, and third-party assessments. It supports compliance monitoring, regulatory change management, and policy analysis.
Predictive analytics
Predictive analytics uses historical and real-time data to forecast potential risks and compliance breaches. It supports proactive monitoring and enables organizations to proactively manage risks before they materialize.
Emerging AI technologies
New AI technologies are shaping the future of GRC, enhancing capabilities beyond current tools:
- Generative AI: Automates drafting of policies and compliance documentation and simulates risk scenarios for strategic planning.
- Explainable AI (XAI): Improves transparency in AI decisions, helping teams and regulators understand how outputs are generated.
- Agentic AI: Enables autonomous monitoring of compliance obligations and emerging risks, triggering workflows with minimal human intervention.
Top 20 AI GRC Software
Below are some notable AI GRC tools, with key focus and score on B2B reviews:
Sprinto
An AI-driven compliance platform for startups and SMBs. Sprinto offers AI-powered features like:
- Autonomous agent architecture: AI agents that proactively fix compliance drifts and align controls/policies without manual intervention.
- Infinite regulatory framework mapping: AI interprets regulatory changes and auto-maps criteria to controls and policies across unlimited frameworks.
- Real-time evidence synthesis: Automatically updates and validates evidence in the background so you’re audit-ready without manual data pulls.
Vanta
A compliance automation tool popular with startups and small businesses. Vanta’s key features include:
- Focus on continuous security posture: Rapid realtime drift detection optimized for cloud-native environments (fastest to SOC2 readiness)
Secureframe
A compliance automation platform for continuous monitoring. Secureframe can deliver:
- Guided audit partner introduction: Platform introduces you to audit firms and templates geared toward audit success stage-by-stage.
- Structured risk score templates: Predefined scoring and mitigation workflows tailored for standard frameworks beyond basic registers.
AuditBoard
An audit, risk, and compliance platform embedding generative AI and automation. AuditBoard features are:
- AI-trained enterprise risk analytics: AuditBoard leverages domain-trained AI to generate insights and narrative content across audit, risk, and compliance.
- Unified audit-risk-ESG linkage: Single model linking control effectiveness with ESG and broader business risk metrics in one data core.
Drata
A continuous control monitoring platform for automated compliance. Some of Drata features include:
- Trust Center with live control health: Publicly accessible and live control health dashboards to demonstrate compliance to stakeholders.
- Pre-mapped risk frameworks and scoring: Built-in risk taxonomy that ties risks automatically to controls and real-time evidence streams.
Diligent One
An enterprise GRC suite for risk and audit management. It delivers:
- Board-integrated risk oversight: Unique ability to seamlessly integrate governance risk insights directly into board management and stakeholder reporting.
- Proprietary governance benchmarking data: Shared analytics on shareholder trends and governance practices used for executive decision support.
Hyperproof
A compliance operations platform with emphasis on integration and automation. It offers:
- GRC Maturity Model guidance: Embedded maturity roadmap that helps assess and benchmark GRC maturity levels across the organization.
- Hyperproof AI assisted guided setup: AI accelerates program setup with smart templates and tailored implementation plans.
- Out-of-box framework library (120+)
LogicGate Risk Cloud
A no-code GRC workflow automation platform. Its key features are:
- Low-code workflow modeler: Drag-and-drop design for custom GRC processes not available in many rigid tools.
- Modular test-once, comply-many architecture: Create reusable workflows that serve multiple frameworks without redesign.
ServiceNow GRC
A cloud-native GRC solution integrated with ITSM. It involves capabilities like:
- ITSM-embedded risk and compliance automation: Deep integration with IT service workflows (policy to incident), unlike GRC tools that are siloed.
- Operational resilience planning: Native support for business impact analysis and continuity planning as part of GRC.
Resolver GRC
A GRC platform emphasizing AI-driven risk intelligence. Its typical strengths include incident-to-risk linkage and security intelligence connectivity.
LogicManager
A mid-market GRC platform focusing on usability and targeted AI-assisted features. AI capabilities are:
- Operational resilience risk modeling: Built-in operational and enterprise risk methods that go beyond typical GRC register frameworks.
- Incident management linked to risk score evolution: Track incidents and see their propagated effect on risk posture in models.
SAP GRC
A governance, risk, and compliance suite designed for SAP environments. Some of the top capabilities of SAP GRC involves:
- Tight SAP ecosystem enforcement: Native governance, risk, and compliance across ECC/S/4HANA modules tied to real enterprise transactional data.
- Embedded control enforcement in live business processes: Detect and prevent violations within core ERP transactions rather than in isolated compliance modules
IBM OpenPages
An enterprise risk management solution with AI insights. Some of the top features of IBM include:
- Agentic AI compliance recommendations: AI gives intelligent applicability suggestions for controls and compliance applicability.
- Advanced AI + predictive risk modeling: Integrates with Cognos for self-service predictive analytics that project risk exposures and control gaps.
AI GRC Use Cases
Real-life AI in GRC use cases include:
AI in risk management
Traditional risk management relies on historical data and periodic reviews, which can delay visibility into changing conditions. AI enables forward-looking analysis by continuously evaluating data and modeling risk scenarios across operational and external inputs.
Machine learning models assign dynamic risk scores, detect anomalies, and surface early indicators of emerging threats. This allows faster prioritization and supports timely decision-making when risks affect multiple business areas.
AI in compliance management
Compliance functions often depend on manual coordination and static reporting. AI introduces automation across compliance management activities, improving consistency and reducing dependency on manual workflows.
AI tools continuously test controls across systems, identifying existing controls gaps. By mapping internal controls to regulatory requirements, AI helps organizations maintain compliance while reducing the effort required to update compliance documentation for audits and reviews.
AI in audit and governance
Internal audit activities are typically retrospective and resource intensive. AI enables continuous evaluation and risk-based prioritization of audit efforts.
In governance, AI analyzes audit trails, financial records, and operational data to detect anomalies that indicate potential compliance violations. This supports earlier intervention and improves transparency across audit and oversight functions.
AI in cyber risk management
As threats increase in complexity, traditional rule-based tools struggle to keep pace. AI strengthens cyber risk management by learning baseline system behavior and identifying deviations that may signal malicious activity.
By correlating signals from network logs, identity systems, and threat intelligence feeds, AI improves detection accuracy and helps security teams focus on material threats rather than false alerts.
AI in third-party risk management
Vendors and partners can introduce significant exposure. AI improves third-party oversight by automating assessments and enabling continuous monitoring.
During onboarding, AI evaluates vendor data against industry and government regulations to generate real-time risk profiles. Ongoing monitoring detects changes in risk status, supporting earlier intervention and more informed vendor management decisions.
AI in risk and compliance operations
AI supports integrated risk and compliance management by embedding intelligence directly into operational processes. Data from risk, compliance, audit, and IT functions is analyzed together to provide a consolidated view of exposure.
This integrated approach strengthens AI compliance by ensuring regulatory expectations are tracked consistently and controls are monitored across the organization.
Based on the detailed implementation data, here are the case studies categorized by their core activity and structured according to your requirements:
Case study: Mastercard (Global AI Governance Team)
Mastercard is a global leader in financial services and payments technology that manages risks across thousands of models every day. The firm faced novel risk profiles while navigating the rapid proliferation of GenAI They utilized Credo AI for a centralized AI registry and customized intake questionnaires to improve the accountability for the AI lifecycle.
- Results:
- Automated initial risk assessments and evidence collection, allowing for rapid scaling of AI initiatives without increasing administrative head-counts.
- Established a centralized registry for hundreds of Generative AI use cases, providing executive leaders with 100% visibility into internal and external AI adoption.
- Created a persistent audit trail of reviews and approvals across Security, Legal, Privacy, and Brand functions to ensure compliance with enterprise ethics principles.
Industry Analyst
Hazal Şimşek
Industry Analyst
Hazal is an industry analyst at AIMultiple, focusing on process mining and IT automation.
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