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CONTROL LIBRARY
Stop building compliance controls from scratch. Access 315 pre-built, expert-designed controls covering every major AI regulation—ready to implement in your organization today.
What is a Control Library?
A comprehensive collection of pre-built, actionable controls that operationalize regulatory requirements into implementable practices.
AI regulations are complex and constantly evolving:
- ×Vague requirements need translation into concrete actions
- ×Building controls from scratch takes 6-12 months
- ×Risk of missing critical requirements
- ×No standardization across the organization
Our Control Library provides ready-to-use controls:
- Pre-mapped to specific regulatory requirements
- Implementation guidance with step-by-step instructions
- Testing procedures and evidence templates
- Consistent standards across all AI systems
315 Controls Across 9 Major Framework Categories
Comprehensive coverage of EU AI Act, GDPR, NIST AI RMF, CCPA, HIPAA, and critical governance domains. Each control includes detailed implementation guidance, testing procedures, and evidence templates.
Risk Classification Assessment
Evaluate AI system risk level (Minimal, Limited, High, Unacceptable)
Conformity Assessment Process
Third-party assessment for high-risk AI systems
Technical Documentation
Comprehensive system documentation including design, development, and testing
Human Oversight Requirements
Implement human-in-the-loop mechanisms for critical decisions
Transparency Obligations
Disclosure requirements for AI system users
Quality Management System
Establish QMS for high-risk AI systems per Article 17
Post-Market Monitoring
Continuous monitoring of AI system performance in production
Incident Reporting
Report serious incidents and malfunctions to authorities
Data Governance
Training, validation, testing datasets governance (Article 10)
Record-Keeping
Automatic logging of high-risk AI system events (Article 12)
Lawful Basis Documentation
Document legal basis for personal data processing
Data Protection Impact Assessment (DPIA)
Assess privacy risks for high-risk processing
Data Minimization
Collect only necessary personal data
Right to Explanation
Provide meaningful information about automated decisions (Article 22)
Data Subject Rights Management
Process for access, rectification, erasure requests
Consent Management
Obtain and document valid consent for data processing
Data Breach Notification
72-hour breach notification process
Privacy by Design
Embed privacy into system architecture
Data Retention Policies
Define and enforce retention schedules
Third-Party Processor Agreements
DPAs with all data processors (Article 28)
AI Risk Mapping
Map AI risks to organizational context and priorities
Performance Measurement
Establish metrics for AI system performance and reliability
Risk Management Plan
Documented approach to managing identified AI risks
Governance Framework
Organizational structure for AI oversight and accountability
Continuous Monitoring
Ongoing tracking of AI system performance and risks
Trustworthiness Characteristics
Assess validity, reliability, safety, security, resilience
AI Inventory Management
Maintain comprehensive registry of AI systems
Stakeholder Engagement
Include diverse perspectives in AI development
Impact Assessment
Evaluate societal and environmental impacts
Transparency Documentation
Document model cards, datasheets, system cards
Protected Attribute Testing
Test for bias across demographics (race, gender, age)
Demographic Parity Analysis
Ensure equal outcomes across protected groups
Equal Opportunity Assessment
Verify equal true positive rates across groups
Disparate Impact Testing
Measure and mitigate adverse impact on protected classes
Fairness Metrics Dashboard
Ongoing monitoring of fairness indicators
Intersectional Bias Testing
Test for bias across multiple protected attributes
Training Data Bias Audit
Assess representation and balance in training datasets
Algorithmic Impact Assessment
Evaluate potential discriminatory effects
Bias Mitigation Strategies
Pre-processing, in-processing, post-processing techniques
Fairness-Aware Feature Engineering
Remove or transform potentially biased features
Consumer Rights Portal
Mechanism for consumers to exercise privacy rights
Do Not Sell/Share Opt-Out
Honor consumer requests not to sell personal information
Privacy Notice Requirements
Disclose data collection and sharing practices
Deletion Request Processing
Delete consumer data upon verified request
Right to Know Implementation
Provide consumers with collected data categories
Service Provider Agreements
Contracts restricting third-party data use
Sensitive Personal Information Limits
Honor limits on sensitive data use
Automated Decision-Making Disclosure
Inform consumers of profiling and automated decisions
Consumer Request Verification
Authenticate consumer identity before fulfilling requests
Non-Discrimination Protection
Ensure equal service regardless of privacy choices
PHI Access Controls
Restrict AI system access to authorized personnel only
Encryption at Rest & Transit
Encrypt protected health information throughout lifecycle
Audit Logging
Log all PHI access, modifications, and disclosures
Business Associate Agreements
BAAs with all third parties processing PHI
Minimum Necessary Standard
Limit PHI use to minimum required for purpose
De-Identification Procedures
Expert determination or safe harbor de-identification
Breach Notification Process
Notify affected individuals within 60 days
Workforce Training
Annual HIPAA compliance training for AI teams
Risk Assessment
Regular HIPAA Security Rule risk analysis
Contingency Planning
Data backup and disaster recovery for PHI
Model Development Lifecycle
Standardized SDLC for AI model development
Model Risk Tiering
Classify models by risk (High/Medium/Low)
Model Approval Workflow
Review and approval gates before deployment
Model Registry
Central repository of all production AI models
Version Control
Track model versions, code, datasets, configurations
Change Management
Controlled process for model updates
Model Retirement
Decommission obsolete or non-compliant models
Ownership & Accountability
Assign model owners and stewards
Model Documentation Standards
Model cards, datasheets, technical specifications
Third-Party Model Due Diligence
Vet external models and APIs
Feature Importance Analysis
Identify most influential input features
SHAP/LIME Explanations
Local interpretability for individual predictions
Global Model Behavior
Understand overall model decision patterns
Counterfactual Explanations
What changes would alter the prediction?
Decision Rules Extraction
Derive interpretable rules from complex models
Attention Visualization
Show which inputs the model focuses on
Saliency Maps
Highlight important regions in images
Explanation User Interfaces
User-friendly explanation dashboards
Explanation Quality Metrics
Measure explanation accuracy and usefulness
Stakeholder Explanation Customization
Tailor explanations to audience
Adversarial Robustness Testing
Test against adversarial attacks
Input Validation
Sanitize and validate all model inputs
Model Extraction Defense
Protect against model stealing attacks
Data Poisoning Detection
Identify corrupted training data
Backdoor Detection
Scan for trojan triggers in models
Inference Privacy
Prevent membership inference attacks
Differential Privacy
Add noise to protect individual privacy
Secure Model Deployment
Encrypt models in production
API Security
Rate limiting, authentication, authorization
Prompt Injection Defense
Protect LLMs from malicious prompts
Complete Control Library Includes
Each control includes detailed implementation steps, testing procedures, evidence templates, and regulatory mappings.
Access Full Control LibraryWhy Organizations Love Our Control Library
Transform compliance from a burden into a competitive advantage.
Real-World Success Stories
See how organizations across industries achieve compliance faster with our Control Library.
Challenge:
Comply with EU AI Act for credit scoring and fraud detection systems
Solution:
Applied 47 pre-built controls from EU AI Act and Fairness categories
Result:
Achieved compliance 6 months ahead of deadline, passed regulatory audit on first attempt
Challenge:
HIPAA compliance for AI diagnostic tools while meeting GDPR requirements
Solution:
Implemented integrated control library spanning HIPAA, GDPR, and EU AI Act
Result:
Reduced compliance team workload by 70%, launched AI products in EU and US simultaneously
Challenge:
Ensure fairness in AI recommendation and pricing algorithms
Solution:
Deployed fairness controls with automated bias testing across 12 demographic groups
Result:
Eliminated discriminatory patterns, increased customer trust, avoided regulatory fines
How to Use the Control Library
Simple 4-step process to implement compliance controls across your organization.
