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NIST AI Risk Management Framework Checklist ​

Overview ​

The NIST AI Risk Management Framework (AI RMF) is a voluntary framework designed to help organizations manage risks related to artificial intelligence. Published by the National Institute of Standards and Technology (NIST) in January 2023, it provides a structured approach to building trustworthy AI systems.

Who should use NIST AI RMF:

  • U.S. federal agencies (following OMB guidance on AI governance)
  • Private sector organizations using or developing AI
  • Organizations seeking a risk-based approach to AI governance
  • Companies preparing for future AI regulations (framework aligns with emerging laws)

What NIST AI RMF covers:

  • Four core functions: Govern, Map, Measure, Manage (structured approach to AI risk management)
  • Seven characteristics of trustworthy AI: Valid & reliable, Safe, Secure & resilient, Accountable & transparent, Explainable & interpretable, Privacy-enhanced, Fair with harmful bias managed
  • Risk-based approach: Adapt to organization's risk appetite, resources, and AI maturity
  • Lifecycle perspective: Address AI risks from conception through retirement

How SignalBreak supports NIST AI RMF:

SignalBreak helps organizations implement NIST AI RMF by providing continuous monitoring, risk detection, and governance documentation for AI systems—especially third-party AI providers that organizations depend on but don't directly control.

How to Use This Checklist ​

  1. Assess current state: Review each action and outcome, marking your organization's status
  2. Prioritize gaps: Focus on high-risk AI systems and critical functions first
  3. Implement controls: Use SignalBreak and organizational policies to address gaps
  4. Document progress: Maintain evidence of risk management activities
  5. Review regularly: NIST AI RMF is a continuous cycle—revisit periodically

Checklist symbols:

  • ✅ SignalBreak helps directly: Platform feature supports this action
  • 📋 SignalBreak provides evidence: Platform generates documentation
  • ⚙️ Organization policy required: Must establish policy or process (SignalBreak can enforce)

NIST AI RMF: Four Core Functions ​

The framework is organized around four functions that represent a lifecycle approach to AI risk management:

  1. GOVERN: Establish culture, processes, and structures for responsible AI
  2. MAP: Identify and document AI use cases, context, impacts, and risks
  3. MEASURE: Assess AI system performance, trustworthiness, and risk levels
  4. MANAGE: Implement controls, monitor continuously, and respond to incidents

Each function contains Categories and Subcategories that define specific actions and outcomes.

GOVERN Function ​

Purpose: Cultivate organizational culture and establish structures to enable responsible AI development and use.

Category GOVERN 1: Policies, Processes, Procedures, and Practices ​

Objective: Establish governance frameworks that integrate AI risk management into organizational operations.

Action: Understand and document applicable legal and regulatory requirements for AI.

What this means:

  • Identify laws, regulations, and standards that apply to your AI use (industry-specific, data privacy, discrimination, safety)
  • Monitor for new or changing AI regulations
  • Ensure AI systems comply with requirements

Checklist:

  • [ ] Regulatory inventory completed:

    • [ ] Federal regulations identified (OMB M-24-10 for agencies, FTC Act, ECOA, ADA, etc.)
    • [ ] State regulations reviewed (CCPA, AI-specific state laws)
    • [ ] Industry regulations documented (HIPAA for healthcare, SR 11-7 for finance, etc.)
    • [ ] International regulations considered if applicable (GDPR, EU AI Act)

  • [ ] Compliance requirements documented:

    • [ ] Requirements mapped to specific AI systems
    • [ ] Compliance gaps identified
    • [ ] Remediation plans created

  • [ ] Regulatory monitoring established:

    • [ ] Process to track new/changing AI regulations
    • [ ] Responsibility assigned for regulatory updates

SignalBreak support:

  • 📋 Governance documentation: Evidence packs demonstrate AI governance for regulatory compliance
  • ⚙️ Compliance tracking: Organization must track regulations; SignalBreak provides evidence of implementation

GOVERN 1.2: Organizational AI Risk Management Strategy ​

Action: Establish an AI risk management strategy aligned with organizational values and risk appetite.

What this means:

  • Define how AI fits into organizational strategy
  • Establish risk appetite for AI (how much risk is acceptable)
  • Create governance structure (who is responsible for AI risk management)

Checklist:

  • [ ] AI strategy documented:

    • [ ] AI vision and goals aligned with organizational mission
    • [ ] AI use cases prioritized based on value and risk
    • [ ] Resources allocated for AI governance

  • [ ] Risk appetite defined:

    • [ ] Risk tolerance for different AI impact levels (high-risk vs. low-risk)
    • [ ] Criteria for AI approval, escalation, and discontinuation
    • [ ] Balance between innovation and caution established

  • [ ] Governance structure established:

    • [ ] AI governance committee or steering group formed
    • [ ] Roles and responsibilities assigned (AI owners, risk managers, compliance)
    • [ ] Escalation paths defined

SignalBreak support:

  • ✅ Organization structure: Define governance via scenarios (AI systems) and roles (Admins, Members, Viewers)
  • 📋 Risk reports: Dashboard shows AI risks to inform strategy discussions

GOVERN 1.3: Organizational AI Risk Management Processes ​

Action: Establish processes for managing AI throughout its lifecycle.

What this means:

  • Define how AI is developed, deployed, monitored, and retired
  • Integrate AI risk management into existing processes (IT, security, compliance)
  • Document procedures and assign ownership

Checklist:

  • [ ] Lifecycle processes documented:

    • [ ] AI development: Requirements, design, testing, approval
    • [ ] AI deployment: Production release, rollback, change management
    • [ ] AI monitoring: Performance, accuracy, fairness, incidents
    • [ ] AI retirement: Decommissioning, data disposal, documentation

  • [ ] Integration with existing processes:

    • [ ] AI risk management integrated with enterprise risk management (ERM)
    • [ ] AI security integrated with information security program
    • [ ] AI privacy integrated with privacy program
    • [ ] AI procurement integrated with vendor management

SignalBreak support:

  • ✅ Monitoring process: Continuous signal detection supports AI monitoring lifecycle phase
  • 📋 Audit trail: Logs document governance activities across AI lifecycle

GOVERN 1.4: Roles and Responsibilities ​

Action: Clearly define and communicate roles and responsibilities for AI risk management.

What this means:

  • Assign accountability for AI governance (who decides, who executes, who is informed)
  • Ensure roles have authority and resources
  • Document responsibilities and communicate to team

Checklist:

  • [ ] Roles defined and assigned:

    • [ ] Executive sponsor (accountable for AI strategy and risk)
    • [ ] AI governance committee (oversight and policy)
    • [ ] AI system owners (accountable for specific AI systems)
    • [ ] AI risk manager (assesses and mitigates AI risks)
    • [ ] Data stewards (manage data for AI)
    • [ ] Security and compliance officers (ensure AI meets requirements)

  • [ ] Responsibilities documented:

    • [ ] RACI matrix (Responsible, Accountable, Consulted, Informed)
    • [ ] Authority defined (who can approve, pause, or retire AI systems)
    • [ ] Escalation paths clear

  • [ ] Communication completed:

    • [ ] Roles communicated to team
    • [ ] Training provided on responsibilities
    • [ ] Roles reviewed annually

SignalBreak support:

  • ✅ Role-based access: Admins (governance), Members (operations), Viewers (monitoring)
  • ✅ Alert routing: Signals escalate to appropriate stakeholders

GOVERN 1.5: Organizational Policies for AI ​

Action: Establish policies that guide responsible AI use.

What this means:

  • Written policies define expectations for AI (ethics, fairness, transparency, security)
  • Policies approved by leadership and communicated to staff
  • Policies enforced through processes and controls

Checklist:

  • [ ] ⚙️ AI policy established:

    • [ ] Purpose and scope defined
    • [ ] Principles for responsible AI articulated (fairness, transparency, accountability)
    • [ ] Prohibited AI uses identified (e.g., discriminatory AI, surveillance without consent)
    • [ ] Requirements for AI approval and oversight
    • [ ] Policy approved by leadership

  • [ ] Policy communicated:

    • [ ] Published to staff and contractors
    • [ ] Training provided on policy
    • [ ] Policy accessible (intranet, handbook)

  • [ ] Policy enforced:

    • [ ] Processes ensure policy compliance (e.g., impact assessments before deployment)
    • [ ] Non-compliance addressed (investigation, corrective action)
    • [ ] Policy reviewed annually and updated

SignalBreak support:

  • ⚙️ Policy enforcement: SignalBreak enforces monitoring and alerting policies (once defined)
  • 📋 Compliance evidence: Reports show policy implementation (e.g., "100% of production AI monitored")

GOVERN 1.6: Accountability Structures ​

Action: Establish accountability for AI outcomes.

What this means:

  • Clear ownership for each AI system (someone is accountable for its performance and impacts)
  • Mechanisms to hold owners accountable (performance reviews, incentives, consequences)
  • Escalation when accountability fails

Checklist:

  • [ ] Ownership assigned:

    • [ ] Each AI system has a named owner (individual or team)
    • [ ] Owner accountable for performance, fairness, security, compliance
    • [ ] Ownership documented and communicated

  • [ ] Accountability mechanisms established:

    • [ ] AI performance included in owner's objectives and reviews
    • [ ] Incentives align with responsible AI outcomes
    • [ ] Consequences for non-compliance or negligence
    • [ ] Escalation to leadership when risks emerge

SignalBreak support:

  • ✅ Scenario ownership: Assign owners to scenarios (AI systems) for accountability
  • 📋 Audit trail: Documents who took action (or failed to act) on AI risks

GOVERN 1.7: Workforce AI Literacy ​

Action: Ensure workforce has knowledge and skills to engage responsibly with AI.

What this means:

  • Staff understand AI capabilities, limitations, and risks
  • Training appropriate to role (executives, AI developers, AI users, general staff)
  • Continuous learning as AI evolves

Checklist:

  • [ ] Training program established:

    • [ ] AI literacy training for all staff (what is AI, how it's used, risks)
    • [ ] Role-specific training (developers: responsible AI development; users: interpreting AI outputs; governance: risk assessment)
    • [ ] Compliance training (legal, ethical, regulatory requirements)

  • [ ] Training delivered:

    • [ ] Initial training for new hires
    • [ ] Refresher training (annual or after incidents)
    • [ ] Completion tracked

  • [ ] Competence verified:

    • [ ] Assessments or quizzes
    • [ ] Certification for high-risk roles
    • [ ] Gaps addressed through additional training

SignalBreak support:

  • ✅ User-friendly platform: Reduces training burden (intuitive interface)
  • 📋 Real-time learning: Signals provide on-the-job learning about AI risks

Category GOVERN 2: Accountability and Transparency ​

Objective: Ensure AI systems are accountable and transparent to stakeholders.

GOVERN 2.1: Documentation of AI System Development and Use ​

Action: Maintain comprehensive documentation of AI systems.

What this means:

  • Document AI purpose, design, data, performance, and risks
  • Keep documentation current as AI changes
  • Make documentation accessible to relevant stakeholders

Checklist:

  • [ ] AI system documentation created:

    • [ ] Purpose and use case
    • [ ] Data sources (training data, operational data)
    • [ ] Model architecture and algorithms
    • [ ] Performance metrics and limitations
    • [ ] Known risks and mitigation measures
    • [ ] Version history and change log

  • [ ] Documentation maintained:

    • [ ] Updated when AI system changes
    • [ ] Version control applied
    • [ ] Obsolete documentation archived

  • [ ] Documentation accessible:

    • [ ] Stored in centralized repository
    • [ ] Available to AI owners, governance, auditors, and relevant stakeholders
    • [ ] Searchable and well-organized

SignalBreak support:

  • 📋 AI inventory: Automatically maintains list of AI systems (scenarios) and providers
  • 📋 Change logs: Logs AI provider model updates (version history)
  • 📋 Evidence packs: Compile documentation for stakeholders or auditors

GOVERN 2.2: Disclosure of AI Use ​

Action: Disclose AI use to affected individuals and stakeholders where appropriate.

What this means:

  • Inform people when they interact with AI (e.g., chatbots, automated decisions)
  • Explain how AI is used and what data it processes
  • Provide transparency about AI limitations and risks

Checklist:

  • [ ] Disclosure requirements defined:

    • [ ] Identify which AI systems require disclosure (customer-facing, high-stakes decisions)
    • [ ] Determine what to disclose (AI use, data collected, decision logic, human review)
    • [ ] Establish disclosure methods (UI messages, terms of service, privacy notices)

  • [ ] Disclosures implemented:

    • [ ] Notifications added to AI-powered interfaces
    • [ ] Privacy notices updated to describe AI use
    • [ ] FAQs or help content explains AI to users

  • [ ] Feedback mechanisms provided:

    • [ ] Users can ask questions about AI
    • [ ] Users can challenge or appeal AI decisions
    • [ ] Feedback incorporated into AI improvement

SignalBreak support:

  • ⚙️ Transparency: Organization must implement disclosure; SignalBreak provides AI inventory to inform disclosure
  • 📋 Documentation: Reports show which AI providers are used (supports transparency)

GOVERN 2.3: Explainability and Interpretability ​

Action: Design AI systems to be explainable and interpretable where appropriate.

What this means:

  • High-stakes AI (benefits, employment, credit, enforcement) should provide explanations for decisions
  • Explanations appropriate to audience (technical for developers, plain language for users)
  • Balance explainability with performance (simpler models may be more explainable)

Checklist:

  • [ ] Explainability requirements defined:

    • [ ] Identify which AI systems require explanations (high-stakes, legally mandated)
    • [ ] Define level of explanation needed (feature importance, decision path, plain-language summary)

  • [ ] Explainability implemented:

    • [ ] Explainable AI techniques used (LIME, SHAP, attention mechanisms)
    • [ ] Explanation interfaces built (dashboards, reports, user-facing messages)
    • [ ] Explanations tested with target audiences for comprehension

  • [ ] Limitations documented:

    • [ ] Cases where explanations are partial or unavailable
    • [ ] Trade-offs between explainability and performance acknowledged

SignalBreak support:

  • ⚙️ Explainability: Organization must implement; SignalBreak logs which AI model versions were used (supports explainability for decisions)
  • 📋 Audit trail: Documents AI provider and model version for each time period (enables tracing decisions)

Category GOVERN 3: Diversity, Equity, Inclusion, and Accessibility (DEIA) ​

Objective: Ensure AI systems promote fairness and accessibility.

GOVERN 3.1: Diversity and Inclusion in AI Teams ​

Action: Foster diversity in teams that design, develop, and deploy AI.

What this means:

  • Diverse teams bring varied perspectives that reduce blind spots and bias
  • Include perspectives from affected communities, ethicists, and domain experts
  • Create inclusive culture where concerns about AI fairness are heard and addressed

Checklist:

  • [ ] Diversity goals established:

    • [ ] Representation goals for AI teams (demographics, disciplines, perspectives)
    • [ ] Recruitment and hiring practices promote diversity
    • [ ] Retention and advancement support diversity

  • [ ] Inclusion practices implemented:

    • [ ] Inclusive culture fostered (psychological safety, diverse voices valued)
    • [ ] Bias awareness training provided
    • [ ] Ethics and fairness concerns welcomed and addressed

  • [ ] Stakeholder engagement:

    • [ ] Affected communities consulted during AI design
    • [ ] Domain experts involved (not just technologists)
    • [ ] Feedback incorporated into AI systems

SignalBreak support:

  • ⚙️ Team diversity: Organization must establish; SignalBreak supports diverse governance teams via role-based access

GOVERN 3.2: Accessibility in AI Systems ​

Action: Ensure AI systems are accessible to people with disabilities.

What this means:

  • AI interfaces comply with accessibility standards (WCAG, Section 508)
  • AI outputs available in accessible formats (screen reader compatible, alternative text, transcripts)
  • AI does not discriminate against people with disabilities

Checklist:

  • [ ] Accessibility requirements defined:

    • [ ] Standards identified (WCAG 2.1 Level AA, Section 508 for federal)
    • [ ] Accessibility requirements documented for AI interfaces

  • [ ] Accessibility implemented:

    • [ ] AI interfaces tested with assistive technologies (screen readers, voice control)
    • [ ] Alternative formats provided (transcripts for audio, alt text for images)
    • [ ] Accessibility issues remediated

  • [ ] Accessibility maintained:

    • [ ] Accessibility tested when AI systems updated
    • [ ] User feedback from people with disabilities solicited and addressed

SignalBreak support:

  • ⚙️ Accessibility: Organization must implement; SignalBreak platform itself follows accessibility best practices
  • 📋 Documentation: Reports support compliance documentation for accessibility audits

Category GOVERN 4: Organizational Transparency ​

Objective: Be transparent with stakeholders about AI governance practices.

GOVERN 4.1: Transparent AI Governance Practices ​

Action: Communicate AI governance practices to stakeholders.

What this means:

  • Publicly share AI principles, policies, and governance structures (where appropriate)
  • Report on AI risk management activities (audits, assessments, incidents)
  • Engage stakeholders in AI governance dialogue

Checklist:

  • [ ] Transparency commitments defined:

    • [ ] Determine what to disclose publicly (AI principles, governance structure, impact assessments)
    • [ ] Balance transparency with proprietary/security concerns
    • [ ] Define reporting cadence (annual AI governance report, incident disclosures)

  • [ ] Transparency implemented:

    • [ ] AI principles and policies published (website, reports)
    • [ ] AI governance structure disclosed (roles, processes)
    • [ ] AI risk management activities reported (audits, incidents, improvements)

  • [ ] Stakeholder engagement:

    • [ ] Channels for stakeholder feedback (surveys, consultations, public comment)
    • [ ] Feedback incorporated into governance practices

SignalBreak support:

  • 📋 Governance reports: Generate reports for public or stakeholder disclosure
  • ✅ Transparency: Dashboard provides internal transparency (governance team visibility)

MAP Function ​

Purpose: Identify and document AI use cases, context, impacts, and risks.

Category MAP 1: Context and Intent ​

Objective: Understand the context in which AI is used and intended purposes.

MAP 1.1: AI Use Cases and Applications ​

Action: Identify and document all AI use cases within the organization.

What this means:

  • Create inventory of AI systems (purchased, developed, piloted)
  • Document purpose, users, and benefits for each AI system
  • Update inventory as AI landscape changes

Checklist:

  • [ ] AI inventory created:

    • [ ] All AI systems identified (production, pilot, research)
    • [ ] For each AI: Purpose, business function, users, data, AI provider
    • [ ] Inventory includes both internally developed and third-party AI

  • [ ] Inventory maintained:

    • [ ] Process to discover new AI (procurement reviews, IT audits, employee surveys)
    • [ ] Inventory updated quarterly or continuously
    • [ ] Retired AI systems removed

  • [ ] Inventory accessible:

    • [ ] Published to governance team, leadership, and auditors
    • [ ] Searchable and filterable

SignalBreak support:

  • ✅ AI inventory: Scenarios provide automatic, continuously updated AI inventory
  • ✅ Discovery: Identify AI usage across organization through scenario monitoring

MAP 1.2: AI System Objectives and Expected Benefits ​

Action: Document intended objectives and benefits of each AI system.

What this means:

  • Why is this AI being used? What problem does it solve?
  • What are success criteria? How will benefits be measured?
  • Are objectives aligned with organizational values and ethics?

Checklist:

  • [ ] Objectives documented for each AI:

    • [ ] Business problem or opportunity addressed
    • [ ] Expected benefits (efficiency, accuracy, cost savings, customer experience)
    • [ ] Success metrics (KPIs, performance targets)

  • [ ] Alignment verified:

    • [ ] AI objectives align with organizational strategy
    • [ ] AI use is consistent with ethical principles and policies
    • [ ] Trade-offs considered (e.g., efficiency vs. explainability)

SignalBreak support:

  • 📋 Scenario descriptions: Document AI objectives in scenario metadata
  • ⚙️ Alignment: Organization defines objectives; SignalBreak monitors performance toward them

MAP 1.3: Stakeholder Analysis ​

Action: Identify stakeholders affected by AI and understand their perspectives.

What this means:

  • Who uses, is affected by, or has interest in this AI?
  • What are their needs, concerns, and expectations?
  • How will they be engaged in AI governance?

Checklist:

  • [ ] Stakeholders identified:

    • [ ] End users of AI (customers, employees, citizens)
    • [ ] People affected by AI decisions (benefit recipients, loan applicants, enforcement targets)
    • [ ] Internal stakeholders (developers, business owners, compliance, leadership)
    • [ ] External stakeholders (regulators, advocacy groups, media, public)

  • [ ] Perspectives documented:

    • [ ] Needs and expectations of each stakeholder group
    • [ ] Concerns and risks perceived by stakeholders
    • [ ] Power dynamics and vulnerable populations identified

  • [ ] Engagement planned:

    • [ ] How stakeholders will be consulted during AI design and operation
    • [ ] How feedback will be collected and incorporated
    • [ ] How stakeholders will be informed of AI changes or incidents

SignalBreak support:

  • ⚙️ Stakeholder engagement: Organization must establish; SignalBreak alerts notify stakeholders of AI incidents
  • 📋 Communication: Reports provide information for stakeholder updates

Category MAP 2: Context and Risk Assessment ​

Objective: Assess AI risks in context.

MAP 2.1: Potential Benefits and Costs ​

Action: Evaluate potential benefits and costs of AI use.

What this means:

  • Beyond intended benefits, what are unintended consequences?
  • Who benefits from AI? Who bears costs or risks?
  • Are benefits distributed equitably?

Checklist:

  • [ ] Benefits assessed:

    • [ ] Direct benefits (efficiency, accuracy, cost savings)
    • [ ] Indirect benefits (improved decision-making, insights, innovation)
    • [ ] Beneficiaries identified (organization, customers, employees, society)

  • [ ] Costs assessed:

    • [ ] Direct costs (development, licensing, infrastructure, maintenance)
    • [ ] Indirect costs (risks, harms, opportunity costs)
    • [ ] Cost bearers identified (who pays financially, who bears risks)

  • [ ] Equity considered:

    • [ ] Benefits distributed fairly across stakeholder groups?
    • [ ] Disadvantaged groups disproportionately bearing costs or risks?
    • [ ] Mitigation for inequitable distributions?

SignalBreak support:

  • ⚙️ Cost-benefit analysis: Organization must conduct; SignalBreak provides data on AI provider costs and risks
  • 📋 Risk reports: Inform cost-benefit discussions with AI risk data

MAP 2.2: Identification of AI Risks ​

Action: Identify potential AI risks in context.

What this means:

  • What can go wrong with this AI? (technical failures, misuse, unintended consequences)
  • Who might be harmed? How severe are potential harms?
  • What is likelihood of risks materializing?

Checklist:

  • [ ] Risk types identified:

    • [ ] Technical risks: Errors, bias, inaccuracy, outages, model drift, adversarial attacks
    • [ ] Misuse risks: AI used for unauthorized or unethical purposes
    • [ ] Societal risks: Discrimination, privacy violations, job displacement, environmental impact
    • [ ] Vendor risks: Third-party AI provider failures, security breaches, vendor lock-in

  • [ ] Harms assessed:

    • [ ] Potential harms to individuals (discrimination, privacy breach, physical harm, economic loss)
    • [ ] Potential harms to organization (legal liability, reputational damage, financial loss, operational disruption)
    • [ ] Potential harms to society (erosion of trust, social inequality, environmental harm)

  • [ ] Likelihood and severity evaluated:

    • [ ] Likelihood of each risk (rare, possible, likely, almost certain)
    • [ ] Severity of harm if risk materializes (negligible, minor, moderate, major, catastrophic)
    • [ ] Risk level calculated (likelihood × severity)

SignalBreak support:

  • ✅ Risk detection: Automatically identifies AI risks (outages, model updates, concentration)
  • 📋 Risk reports: Dashboard shows identified risks and severity

MAP 2.3: AI System Components and Dependencies ​

Action: Map AI system components and dependencies.

What this means:

  • What are the building blocks of this AI? (data, model, infrastructure, integrations)
  • What does this AI depend on? (third-party APIs, data sources, human inputs)
  • Where are single points of failure?

Checklist:

  • [ ] Components mapped:

    • [ ] Data: Training data, operational input data, reference data
    • [ ] Model: Algorithm, architecture, version, provider
    • [ ] Infrastructure: Cloud hosting, compute, storage
    • [ ] Integrations: APIs, databases, applications AI connects to
    • [ ] Human elements: Human review, feedback, oversight

  • [ ] Dependencies documented:

    • [ ] Third-party AI providers (OpenAI, Anthropic, Google, etc.)
    • [ ] Data providers and sources
    • [ ] Infrastructure dependencies (cloud regions, availability zones)
    • [ ] Integration dependencies (APIs that must remain available)

  • [ ] Single points of failure identified:

    • [ ] Critical components with no redundancy
    • [ ] Concentration risks (e.g., 80% of AI from one provider)
    • [ ] Mitigation plans for single points of failure

SignalBreak support:

  • ✅ Dependency mapping: Provider bindings show which AI providers each scenario depends on
  • ✅ Concentration risk: Reports identify single points of failure (vendor concentration)

Category MAP 3: AI System Categorization ​

Objective: Categorize AI systems by impact and risk level.

MAP 3.1: AI Impact Level Assessment ​

Action: Assess potential impact of AI on individuals, organizations, and society.

What this means:

  • Classify AI by impact level (high, medium, low) to prioritize governance
  • High-impact AI (affects rights, safety, critical operations) requires more rigorous governance
  • Low-impact AI (administrative, operational support) may need lighter oversight

Checklist:

  • [ ] Impact criteria defined:

    • [ ] High impact: Affects civil rights/liberties, safety, critical decisions (benefits, credit, employment, enforcement), or critical operations
    • [ ] Medium impact: Affects important but not critical decisions, or processes non-sensitive data
    • [ ] Low impact: Administrative, operational support, no significant individual or societal impact

  • [ ] AI systems classified:

    • [ ] Each AI system assigned an impact level
    • [ ] Classification documented and reviewed
    • [ ] Classification triggers governance requirements (high-impact = more rigorous)

  • [ ] Governance tailored to impact:

    • [ ] High-impact AI: Impact assessments, bias testing, human oversight, continuous monitoring
    • [ ] Medium-impact AI: Risk assessment, periodic monitoring
    • [ ] Low-impact AI: Basic inventory and policy compliance

SignalBreak support:

  • ✅ Impact classification: Scenarios can be tagged with impact level for prioritization
  • 📋 Risk-based reporting: Filter dashboards by impact level to focus on high-risk AI

MAP 3.2: Trustworthiness Characteristics Assessment ​

Action: Assess AI systems against NIST's seven characteristics of trustworthy AI.

What this means:

  • Evaluate how well each AI system demonstrates:
    1. Valid and reliable
    2. Safe
    3. Secure and resilient
    4. Accountable and transparent
    5. Explainable and interpretable
    6. Privacy-enhanced
    7. Fair (harmful bias managed)

Checklist:

  • [ ] Assessment framework established:

    • [ ] Criteria defined for each trustworthiness characteristic
    • [ ] Assessment method chosen (qualitative, quantitative, scorecard)
    • [ ] Assessment responsibility assigned

  • [ ] Assessments conducted:

    • [ ] Each AI system assessed against seven characteristics
    • [ ] Gaps identified (where AI falls short of trustworthiness)
    • [ ] Mitigation plans created for gaps

  • [ ] Results used to prioritize:

    • [ ] AI with low trustworthiness scores prioritized for improvement or retirement
    • [ ] Results inform risk management decisions

SignalBreak support:

  • 📋 Trustworthiness evidence: Monitoring and alerts demonstrate "secure and resilient" and "accountable and transparent" characteristics
  • ⚙️ Other characteristics: Organization must assess; SignalBreak provides data on AI provider performance to inform assessment

MEASURE Function ​

Purpose: Assess AI system performance, trustworthiness, and impacts.

Category MEASURE 1: Performance Metrics ​

Objective: Measure AI system performance against intended objectives.

MEASURE 1.1: AI Performance Metrics ​

Action: Define and track performance metrics for AI systems.

What this means:

  • What metrics define success for this AI? (accuracy, precision, recall, latency, uptime)
  • Are metrics tracked continuously?
  • Do metrics align with business objectives and user needs?

Checklist:

  • [ ] Metrics defined for each AI:

    • [ ] Technical performance (accuracy, precision, recall, F1 score, AUC)
    • [ ] Operational performance (uptime, response time, throughput)
    • [ ] Business performance (cost savings, efficiency gains, user satisfaction)

  • [ ] Metrics tracked:

    • [ ] Automated collection where possible (monitoring tools, logs, APIs)
    • [ ] Manual collection for qualitative metrics (user feedback, incident reports)
    • [ ] Historical data retained for trend analysis

  • [ ] Performance evaluated:

    • [ ] Metrics reviewed regularly (weekly, monthly, quarterly)
    • [ ] Trends analyzed (improving, stable, degrading)
    • [ ] Performance compared to baselines and targets

SignalBreak support:

  • ✅ Uptime monitoring: Track AI provider availability (operational performance)
  • ✅ Signal trends: Show AI risk trends over time (informs performance evaluation)
  • 📋 Reports: Provide performance data for analysis

MEASURE 1.2: Model Monitoring and Drift Detection ​

Action: Monitor AI models for performance degradation and drift.

What this means:

  • AI models can degrade over time as data or environments change (model drift)
  • Detect when model performance declines below acceptable thresholds
  • Investigate and remediate drift

Checklist:

  • [ ] Drift detection implemented:

    • [ ] Monitor input data distributions (data drift)
    • [ ] Monitor model output distributions (prediction drift)
    • [ ] Monitor performance metrics (performance drift)
    • [ ] Alert when drift exceeds thresholds

  • [ ] Drift investigation process:

    • [ ] Investigate root cause of drift (data changes, environment changes, model staleness)
    • [ ] Assess impact (is AI still safe and effective?)
    • [ ] Decide action (retrain, recalibrate, retire)

  • [ ] Retraining triggered:

    • [ ] Models retrained when drift detected
    • [ ] Retrained models tested before redeployment
    • [ ] Retraining logged and documented

SignalBreak support:

  • ✅ Provider change detection: Alerts when AI providers update models (potential source of drift)
  • ⚙️ Model drift: Organization must monitor outputs; SignalBreak detects provider-side changes

Category MEASURE 2: Trustworthiness Metrics ​

Objective: Measure AI trustworthiness characteristics.

MEASURE 2.1: Fairness and Bias Testing ​

Action: Measure AI fairness and test for harmful bias.

What this means:

  • Analyze AI outputs by demographic groups (race, sex, age, disability)
  • Calculate fairness metrics (disparate impact, equalized odds, demographic parity)
  • Identify and mitigate bias

Checklist:

  • [ ] Fairness metrics defined:

    • [ ] Relevant protected classes identified (based on use case and regulation)
    • [ ] Fairness metrics chosen (disparate impact ratio, false positive/negative rates by group, demographic parity)
    • [ ] Thresholds established (e.g., disparate impact <0.8 requires investigation)

  • [ ] Bias testing conducted:

    • [ ] AI outputs analyzed by protected class
    • [ ] Fairness metrics calculated
    • [ ] Bias identified (disparate impact, disparate errors, stereotype amplification)

  • [ ] Bias mitigated:

    • [ ] Root cause analyzed (biased training data, proxy discrimination, algorithmic bias)
    • [ ] Mitigation applied (data rebalancing, algorithmic fairness constraints, threshold adjustment)
    • [ ] Effectiveness verified (retest fairness after mitigation)

  • [ ] Testing repeated:

    • [ ] Periodic testing (quarterly or annually)
    • [ ] Testing triggered by model updates, data changes, or incidents

SignalBreak support:

  • ✅ Change alerts: Notify when AI providers update models (trigger bias retesting)
  • 📋 Documentation: Logs show which model versions were tested for bias

MEASURE 2.2: Privacy and Security Assessments ​

Action: Assess AI systems for privacy and security risks.

What this means:

  • Identify privacy risks (data minimization, consent, purpose limitation, security)
  • Assess security controls (access control, encryption, vulnerability management)
  • Conduct privacy impact assessments (PIAs) or data protection impact assessments (DPIAs)

Checklist:

  • [ ] Privacy risks assessed:

    • [ ] Data inventory (what personal data does AI process?)
    • [ ] Lawful basis verified (consent, contract, legitimate interest, legal obligation)
    • [ ] Data minimization applied (only necessary data collected/processed)
    • [ ] Purpose limitation enforced (data used only for stated purpose)
    • [ ] Data retention limits defined

  • [ ] Security controls assessed:

    • [ ] Access control (who can access AI and data?)
    • [ ] Encryption (data at rest, in transit)
    • [ ] Vulnerability management (patching, scanning)
    • [ ] Incident response (breach detection, notification, remediation)

  • [ ] Impact assessments conducted:

    • [ ] PIAs/DPIAs for high-privacy-risk AI
    • [ ] Mitigation measures identified and implemented
    • [ ] Assessments reviewed when AI changes

SignalBreak support:

  • ⚙️ Privacy and security: Organization must implement; SignalBreak tracks AI provider security incidents
  • 📋 Vendor security: Documentation shows which providers process sensitive data

MEASURE 2.3: Explainability and Transparency Assessments ​

Action: Assess whether AI systems provide adequate explanations.

What this means:

  • Can users understand how AI arrived at its output?
  • Are explanations accurate, complete, and useful?
  • Do explanations meet legal or ethical requirements?

Checklist:

  • [ ] Explainability requirements defined:

    • [ ] Which AI systems require explanations (high-stakes, legally mandated)
    • [ ] What level of explanation (feature importance, decision path, counterfactuals)
    • [ ] Who needs explanations (users, regulators, auditors)

  • [ ] Explanations tested:

    • [ ] Explanation accuracy verified (explanations reflect actual model behavior)
    • [ ] Explanation comprehension tested (users understand explanations)
    • [ ] Explanation usefulness assessed (explanations enable meaningful action)

  • [ ] Gaps addressed:

    • [ ] Explanation methods improved where inadequate
    • [ ] Alternative approaches explored (simpler models, hybrid systems)
    • [ ] Limitations disclosed when explanations partial or unavailable

SignalBreak support:

  • 📋 Transparency: Audit trail shows which AI model version was used (supports explainability for decisions)
  • ⚙️ Explanations: Organization must implement; SignalBreak provides context (provider, model, date)

Category MEASURE 3: Risk Measurement ​

Objective: Measure and evaluate AI-related risks.

MEASURE 3.1: Risk Likelihood and Impact Assessment ​

Action: Assess likelihood and impact of identified AI risks.

What this means:

  • For each risk identified in MAP function, evaluate:
    • Likelihood (how probable is this risk?)
    • Impact (how severe would consequences be?)
    • Risk level (likelihood × impact)

Checklist:

  • [ ] Risk assessment methodology established:

    • [ ] Likelihood scale defined (e.g., 1=rare, 2=possible, 3=likely, 4=almost certain)
    • [ ] Impact scale defined (e.g., 1=negligible, 2=minor, 3=moderate, 4=major, 5=catastrophic)
    • [ ] Risk matrix created (likelihood × impact = risk level)
    • [ ] Risk appetite thresholds (which risks require mitigation vs. acceptance)

  • [ ] Risks assessed:

    • [ ] Likelihood evaluated for each identified risk
    • [ ] Impact evaluated (to individuals, organization, society)
    • [ ] Risk level calculated
    • [ ] Risks prioritized by level

  • [ ] Assessments updated:

    • [ ] Reassess when AI changes (model updates, new use cases)
    • [ ] Reassess when context changes (new regulations, incidents)
    • [ ] Periodic reassessment (annually or as defined)

SignalBreak support:

  • ✅ Risk detection: Automatically identifies risks (outages, model updates, concentration)
  • 📋 Risk reports: Provide data for likelihood and impact assessment
  • ✅ Risk prioritization: Critical signals highlight highest-priority risks

MEASURE 3.2: Incident and Near-Miss Tracking ​

Action: Track AI-related incidents and near-misses.

What this means:

  • Log when AI fails, causes harm, or nearly causes harm
  • Analyze incidents to identify root causes and trends
  • Use incident data to improve AI systems and risk management

Checklist:

  • [ ] Incident tracking system established:

    • [ ] Incident definition (what constitutes an AI incident?)
    • [ ] Incident logging process (how, where, by whom)
    • [ ] Incident categorization (technical failure, bias, security, misuse, other)
    • [ ] Incident severity levels

  • [ ] Incidents tracked:

    • [ ] All AI incidents logged
    • [ ] Near-misses logged (incidents caught before harm occurred)
    • [ ] Incident details captured (what happened, root cause, impact, response)

  • [ ] Incident analysis conducted:

    • [ ] Trends analyzed (common root causes, affected AI systems, time patterns)
    • [ ] Lessons learned documented
    • [ ] Improvements identified and implemented

SignalBreak support:

  • ✅ Incident detection: Signals automatically detect AI provider incidents
  • 📋 Incident log: Signal history provides audit trail of incidents
  • ✅ Trend analysis: Dashboard shows incident patterns over time

MANAGE Function ​

Purpose: Implement controls, monitor continuously, and respond to incidents.

Category MANAGE 1: Risk Treatment ​

Objective: Implement controls to mitigate AI risks.

MANAGE 1.1: Risk Mitigation and Control Implementation ​

Action: Implement controls to reduce AI risks to acceptable levels.

What this means:

  • For risks that exceed appetite, implement mitigation measures
  • Controls can be preventive (stop risks before they occur) or detective (detect risks quickly)
  • Verify controls are effective

Checklist:

  • [ ] Risk treatment decisions made:

    • [ ] For each risk: Mitigate, Accept, Transfer (insurance, vendor contractual liability), or Avoid (don't use AI)
    • [ ] Treatment plans documented with owners and timelines

  • [ ] Controls implemented:

    • [ ] Preventive controls: Design choices, input validation, output constraints, human review, fallback systems
    • [ ] Detective controls: Monitoring, alerting, auditing, testing
    • [ ] Corrective controls: Incident response, rollback, model retraining

  • [ ] Control effectiveness verified:

    • [ ] Controls tested (do they work as intended?)
    • [ ] Residual risk assessed (after controls, is risk acceptable?)
    • [ ] Controls monitored for continued effectiveness

SignalBreak support:

  • ✅ Detective controls: Continuous monitoring and alerting (detect AI risks)
  • ✅ Preventive controls: Fallback providers (prevent single points of failure)
  • 📋 Control documentation: Logs show controls in place and functioning

MANAGE 1.2: Contingency Planning ​

Action: Develop contingency plans for AI failures.

What this means:

  • What happens if this AI fails? (outage, errors, bias incident)
  • How do we maintain operations? (fallback systems, manual processes)
  • How do we recover? (restore service, investigate, prevent recurrence)

Checklist:

  • [ ] Contingency plans created:

    • [ ] Failure scenarios identified (AI outage, performance degradation, security breach, bias incident)
    • [ ] Contingency actions defined (switch to fallback AI, manual process, pause operations, notify users)
    • [ ] Roles and responsibilities assigned

  • [ ] Fallback capabilities established:

    • [ ] Alternative AI providers configured
    • [ ] Manual processes documented and staff trained
    • [ ] Failover mechanisms tested

  • [ ] Plans tested:

    • [ ] Tabletop exercises or simulations
    • [ ] Weaknesses identified and addressed
    • [ ] Plans updated based on lessons learned

SignalBreak support:

  • ✅ Fallback providers: Configure alternative AI providers (contingency capability)
  • ✅ Failover testing: Test switching between providers

Category MANAGE 2: Ongoing Monitoring and Response ​

Objective: Continuously monitor AI systems and respond to incidents.

MANAGE 2.1: Continuous AI Monitoring ​

Action: Monitor AI systems continuously for risks and performance issues.

What this means:

  • Real-time or near-real-time monitoring of AI
  • Automated alerts when thresholds exceeded or anomalies detected
  • Human review and investigation of alerts

Checklist:

  • [ ] Monitoring implemented:

    • [ ] Technical monitoring (uptime, latency, errors, resource usage)
    • [ ] Performance monitoring (accuracy, bias, drift)
    • [ ] Security monitoring (unauthorized access, data breaches)
    • [ ] Vendor monitoring (AI provider incidents, model updates)

  • [ ] Alerting configured:

    • [ ] Thresholds defined for critical metrics
    • [ ] Alerts sent to responsible parties
    • [ ] Alert severity levels (informational, warning, critical)
    • [ ] Escalation procedures for urgent alerts

  • [ ] Monitoring reviewed:

    • [ ] Alerts triaged daily
    • [ ] False positives reduced (threshold tuning)
    • [ ] Monitoring coverage expanded as AI landscape evolves

SignalBreak support:

  • ✅ Continuous monitoring: Automatic signal detection 24/7
  • ✅ Alerting: Email notifications for critical signals
  • ✅ Vendor monitoring: Track AI provider incidents and model updates

MANAGE 2.2: Incident Response ​

Action: Respond promptly and effectively to AI incidents.

What this means:

  • When AI fails or causes harm, activate incident response
  • Contain incident, investigate root cause, remediate, communicate, learn
  • Document incident and improvements

Checklist:

  • [ ] Incident response plan established:

    • [ ] Incident response team (roles, contact info, authority)
    • [ ] Response procedures (triage, containment, investigation, remediation, communication)
    • [ ] Escalation criteria (when to escalate to leadership, regulators, public)

  • [ ] Incident response activated when needed:

    • [ ] Incident detected (via monitoring, user reports, media)
    • [ ] Response team convened
    • [ ] Incident contained (pause AI, switch to fallback, mitigate harm)
    • [ ] Investigation conducted (root cause, scope of impact)
    • [ ] Remediation implemented (fix issue, compensate affected parties)
    • [ ] Communication provided (internal, users, regulators, public as appropriate)

  • [ ] Post-incident review conducted:

    • [ ] What happened, why, how was it handled?
    • [ ] What went well, what could improve?
    • [ ] Lessons learned documented
    • [ ] Improvements implemented

SignalBreak support:

  • ✅ Incident detection: Signals trigger incident response workflow
  • 📋 Incident documentation: Signal logs provide evidence for post-incident review
  • ✅ Communication: Alerts notify incident response team immediately

MANAGE 2.3: AI System Maintenance and Updates ​

Action: Maintain AI systems and manage updates safely.

What this means:

  • Keep AI systems current (security patches, performance improvements)
  • Manage AI model updates (test, assess impact, deploy safely)
  • Retire AI systems when no longer needed or safe

Checklist:

  • [ ] Maintenance procedures established:

    • [ ] Regular maintenance windows (security patches, dependency updates)
    • [ ] Testing required before deploying maintenance changes
    • [ ] Rollback plans in case of issues

  • [ ] Update management process:

    • [ ] AI provider model updates detected
    • [ ] Impact assessed (performance, bias, security)
    • [ ] Testing conducted (functional, fairness, regression)
    • [ ] Approval required before production deployment
    • [ ] Updates logged and documented

  • [ ] Retirement process:

    • [ ] Criteria for AI retirement (obsolete, unsafe, cost-ineffective)
    • [ ] Decommissioning procedure (disable AI, archive data, update documentation)
    • [ ] Retirement communicated to stakeholders

SignalBreak support:

  • ✅ Update detection: Alerts when AI providers release model updates
  • 📋 Update log: Audit trail of model versions over time
  • ✅ Change management: Alerts trigger testing and approval workflow

Category MANAGE 3: Continuous Improvement ​

Objective: Continuously improve AI risk management.

MANAGE 3.1: Feedback and Learning ​

Action: Collect feedback and learn from experience to improve AI systems and governance.

What this means:

  • Solicit feedback from users, stakeholders, and team
  • Analyze incidents, near-misses, and audit findings
  • Use insights to improve AI systems, processes, and policies

Checklist:

  • [ ] Feedback mechanisms established:

    • [ ] User feedback (surveys, support tickets, focus groups)
    • [ ] Stakeholder feedback (consultations, advisory boards)
    • [ ] Team feedback (retrospectives, suggestions, incident debriefs)

  • [ ] Feedback analyzed:

    • [ ] Themes identified (common issues, improvement opportunities)
    • [ ] Root causes analyzed
    • [ ] Improvement proposals created

  • [ ] Improvements implemented:

    • [ ] Proposals evaluated and prioritized
    • [ ] Changes deployed (AI system improvements, process changes, policy updates)
    • [ ] Effectiveness measured

SignalBreak support:

  • ✅ Data for learning: Signal trends and incident reports inform improvement initiatives
  • 📋 Documentation: Logs support retrospective analysis

MANAGE 3.2: Performance Optimization ​

Action: Optimize AI system performance based on measurement and feedback.

What this means:

  • Use performance data to identify improvement opportunities
  • Optimize for efficiency, accuracy, fairness, user experience
  • Balance trade-offs (e.g., accuracy vs. explainability, performance vs. cost)

Checklist:

  • [ ] Optimization opportunities identified:

    • [ ] Performance data reviewed (see MEASURE function)
    • [ ] Bottlenecks identified (latency, errors, bias, costs)
    • [ ] Improvement targets set

  • [ ] Optimizations implemented:

    • [ ] Model improvements (retraining, algorithm changes, hyperparameter tuning)
    • [ ] Infrastructure improvements (scaling, caching, edge deployment)
    • [ ] Process improvements (automation, better data, human-AI collaboration)

  • [ ] Results measured:

    • [ ] Performance improvements verified
    • [ ] Trade-offs acceptable
    • [ ] Optimizations documented

SignalBreak support:

  • 📋 Performance data: Dashboards and reports inform optimization decisions
  • ✅ Vendor optimization: Switch to better-performing providers when needed

NIST AI RMF Implementation Roadmap ​

Phase 1: Govern (Months 1-3) ​

Goal: Establish governance foundation for AI risk management.

Actions:

  1. Form AI governance committee and assign roles
  2. Draft AI policy and obtain leadership approval
  3. Conduct regulatory review and document requirements
  4. Deploy SignalBreak and configure organization structure
  5. Train staff on AI governance responsibilities

Deliverables:

  • AI governance structure (committee, roles, responsibilities)
  • AI policy (approved and communicated)
  • Regulatory compliance requirements
  • SignalBreak deployed and team onboarded

Phase 2: Map (Months 4-6) ​

Goal: Identify and document AI use cases, context, and risks.

Actions:

  1. Conduct AI inventory across organization
  2. Classify AI systems by impact level
  3. Identify stakeholders and document perspectives
  4. Map AI risks (technical, vendor, societal)
  5. Configure SignalBreak monitoring for top 20 AI systems

Deliverables:

  • AI inventory (all AI systems documented)
  • Impact level classifications
  • Stakeholder analysis
  • AI risk register
  • SignalBreak scenarios covering high-risk AI

Phase 3: Measure (Months 7-9) ​

Goal: Assess AI performance, trustworthiness, and risks.

Actions:

  1. Define performance metrics for AI systems
  2. Conduct bias testing on high-impact AI
  3. Assess trustworthiness characteristics (valid, safe, secure, fair, transparent, privacy-enhanced, explainable)
  4. Complete privacy and security assessments
  5. Expand SignalBreak monitoring to all in-scope AI

Deliverables:

  • Performance metrics dashboard
  • Bias testing reports
  • Trustworthiness assessments
  • Privacy/security assessments
  • Comprehensive SignalBreak monitoring

Phase 4: Manage (Months 10-12) ​

Goal: Implement controls, monitor continuously, and respond to incidents.

Actions:

  1. Implement risk mitigation controls (fallback providers, human oversight, testing)
  2. Develop contingency plans for AI failures
  3. Establish continuous monitoring and alerting (leverage SignalBreak)
  4. Create incident response playbook and train team
  5. Conduct tabletop exercise simulating AI incident

Deliverables:

  • Risk mitigation controls implemented
  • Contingency plans and fallback capabilities
  • 24/7 monitoring and alerting via SignalBreak
  • Incident response playbook
  • Tabletop exercise results and improvements

Phase 5: Continuous Improvement (Ongoing) ​

Goal: Continuously improve AI risk management based on feedback and experience.

Actions:

  1. Collect feedback from users and stakeholders
  2. Analyze incidents and near-misses for lessons learned
  3. Optimize AI performance based on metrics
  4. Update policies and processes based on lessons learned
  5. Annual AI RMF maturity assessment

Deliverables:

  • Feedback analysis and improvement initiatives
  • Incident post-mortems and corrective actions
  • AI performance optimizations
  • Policy and process updates
  • Annual maturity assessment report

SignalBreak Features Supporting NIST AI RMF ​

NIST AI RMF FunctionSignalBreak FeatureHow It Helps
GOVERN: Organizational structuresOrganization & rolesDefine governance structure (Admins, Members, Viewers)
GOVERN: DocumentationEvidence packs & audit trailsMaintain comprehensive AI governance documentation
MAP: AI inventoryScenariosAutomatic, continuously updated AI inventory
MAP: AI dependenciesProvider bindingsMap which AI providers each scenario depends on
MAP: Risk identificationSignal detectionAutomatically identify AI risks (outages, updates, concentration)
MEASURE: Performance metricsDashboard & reportsTrack AI provider performance and governance metrics
MEASURE: Incident trackingSignal logsComplete audit trail of AI incidents and responses
MANAGE: Risk mitigationFallback providersConfigure alternatives to mitigate vendor risk
MANAGE: Continuous monitoring24/7 signal detectionReal-time monitoring of AI systems and providers
MANAGE: Incident responseEmail alertsImmediate notification when critical AI risks emerge
MANAGE: Change managementModel update alertsDetect AI provider updates and trigger review workflow
MANAGE: Continuous improvementTrend analysisIdentify patterns and improvement opportunities

Next Steps ​

  1. Download this checklist: Use it to assess your NIST AI RMF alignment
  2. Conduct gap analysis: Identify which actions and outcomes are not yet met
  3. Deploy SignalBreak: Start with AI inventory (MAP) and continuous monitoring (MANAGE)
  4. Prioritize actions: Focus on high-impact AI and critical risks first
  5. Track progress: Update checklist regularly and report to leadership
  6. Mature over time: NIST AI RMF is a journey—continuously improve governance practices

Support ​