Organizations deploying AI systems face a critical gap between regulatory requirements and operational reality. While frameworks like the EU AI Act, NIST AI RMF, and GDPR define what organizations must do, they don't explain how to implement these obligations across dozens or hundreds of AI systems. This gap has created an emerging category of software: AI governance framework tools that translate abstract compliance requirements into machine-enforceable controls.
The numbers tell the story clearly. Despite 90 percent of enterprises using AI in daily operations, only 18 percent have fully implemented governance frameworks. This disconnect exposes organizations to regulatory penalties that can reach €35 million or 7 percent of global turnover under the EU AI Act. The AI governance software market — valued at $0.34 billion in 2025 and projected to reach $1.21 billion by 2030 — reflects the urgent shift from policy-centric governance to operationalized oversight.
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AI governance framework tools are software platforms that operationalize the principles and requirements established by regulatory frameworks. Where frameworks like the EU AI Act or NIST AI RMF define obligations—conduct risk assessments, maintain audit trails, implement human oversight—governance tools provide the systems to actually execute these requirements at scale.
The distinction matters because confusion between frameworks and tools creates implementation failures. AI governance frameworks are conceptual structures that define principles, obligations, and standards. The EU AI Act establishes risk-based classifications and conformity requirements. NIST AI RMF organizes governance around four functions: govern, map, measure, and manage. ISO/IEC 42001 provides a management systems standard for AI.
These frameworks answer the "what" and "why" of governance. They tell organizations what risks to assess, what documentation to maintain, and why these practices matter for compliance and trust. But frameworks don't prescribe the "how"—they don't explain how to maintain a living inventory of 200 AI systems, how to conduct continuous bias monitoring, or how to generate machine-readable audit trails that regulators can verify in real time.
AI governance tools fill this operational gap. They provide centralized registries for AI systems, automate risk classification workflows, embed compliance checks into development pipelines, and maintain timestamped evidence of every governance action. Tools translate framework principles into daily workflows that technical teams, compliance officers, and legal counsel can actually use.
Many organizations attempt to govern AI through policy documents and spreadsheet trackers. This approach works briefly but breaks catastrophically as AI portfolios grow. Spreadsheets have no version control, no audit trails, and can't be searched programmatically. When regulators request evidence of compliance with specific requirements, finding relevant information takes weeks.
More fundamentally, manual governance cannot match the velocity of AI development. Data science teams deploy models weekly or daily, while governance committees meet monthly or quarterly. This speed mismatch creates an impossible choice: slow innovation to match governance cycles, or accept unmanaged risk. Organizations increasingly choose the latter, creating shadow AI that operates without oversight.
Regulators now demand continuous, real-time proof rather than point-in-time assertions. The EU AI Act's Article 96 requires organizations to demonstrate compliance through documentary evidence that is machine-readable, timestamped, and continuously updated. A risk assessment completed once at design time cannot capture model drift, data quality degradation, or unintended consequences observed during months of live operation. Only software can maintain this continuous chain of evidence.
Three converging forces make dedicated governance software essential rather than optional: regulatory enforcement, enterprise AI scale, and accountability requirements.
The EU AI Act became partially enforceable in February 2025, with full enforcement for high-risk systems beginning August 2026. Organizations deploying high-risk AI—systems affecting fundamental rights such as credit scoring, hiring, medical diagnosis, or critical infrastructure—face comprehensive obligations including risk management systems, technical documentation, fundamental rights impact assessments, human oversight mechanisms, and registration in EU databases.
Fines reach €35 million or 7 percent of global turnover for serious violations. The European AI Office has begun accepting complaints and conducting assessments, with broad investigative powers to demand technical documentation, conduct model evaluations, and require corrective measures.
GDPR obligations compound these requirements. Privacy by Design (Article 25) requires organizations to integrate privacy protections into system architecture from initial conception. Data Protection Impact Assessments are mandatory for AI systems processing special categories of data or using automated decision-making. The European Data Protection Board's December 2024 Opinion clarifies that AI models trained on personal data are not automatically anonymous and remain subject to GDPR requirements.
Beyond the EU, sectoral regulations impose additional governance obligations. Healthcare AI systems require conformity assessments, clinical evidence, and post-market surveillance. Financial regulators expect governance frameworks aligned with NIST or ISO 42001 standards, with specific requirements for bias detection in lending systems. Education systems using AI for student assessment face high-risk classifications when decisions affect educational access or progression.
Organizations no longer deploy one or two experimental AI models—they operate dozens or hundreds of systems across departments, use cases, and jurisdictions. This scale creates governance challenges that exceed human capacity:
Multiple overlapping regulations apply simultaneously to single AI systems. A medical device using AI must comply with EU AI Act obligations, GDPR requirements, medical device regulations, DORA for e-health platforms with financial components, and potentially NIS 2 for critical infrastructure aspects. Manually coordinating evidence, control implementation, and compliance documentation across these frameworks creates fragmentation and unmanageable complexity.
AI systems change continuously through retraining, data updates, and deployment to new contexts. Governance must adapt in real time rather than being fixed at deployment. Traditional compliance assessment models—annual reviews, point-in-time audits—cannot capture this operational reality.
Regulators increasingly inspect evidence and reject organizations that cannot produce audit-grade proof of their governance systems. The EU AI Act Article 96 establishes an evidence standard requiring that all logs, assessments, and approval trails be machine-readable, version-controlled, timestamped, mapped to regulatory requirements, and continuously updated.
Evidence must demonstrate that governance has translated into actual operational controls. A risk assessment identifying "need for human oversight" must be accompanied by logs showing that oversight actually occurred—records of when humans reviewed decisions, what they approved or rejected, and when their judgments influenced outcomes. Governance without evidence of operation is treated as non-compliance.
Effective governance tools don't merely track abstract principles—they operationalize specific regulatory frameworks that organizations must comply with. Understanding which frameworks apply to your organization determines what capabilities your tools must provide.
The EU AI Act establishes a risk-tiered model that creates explicit governance obligations varying by risk level. AI systems are classified as unacceptable, high-risk, limited-risk, or minimal-risk, with governance intensity proportional to potential harm.
High-risk systems—including biometric identification, credit scoring, hiring decisions, medical diagnosis, and critical infrastructure management—face the most stringent requirements. Organizations must implement comprehensive risk management systems throughout the AI lifecycle, maintain detailed technical documentation, conduct Fundamental Rights Impact Assessments for systems used by public authorities, establish human oversight mechanisms, register systems in EU databases, and maintain audit trails demonstrating system behavior over time.
General-Purpose AI models face distinct obligations focused on transparency and risk management. GPAI providers must document training data, implement governance measures to identify and mitigate systemic risks, and cooperate with regulators through the voluntary Code of Practice framework.
Tools supporting EU AI Act compliance must provide:
GDPR obligations remain critical for AI governance, particularly around privacy rights and algorithmic accountability. Two provisions shape tool requirements most directly:
Privacy by Design (Article 25) requires organizations to integrate privacy protections into system architecture from initial conception. This means conducting Data Protection Impact Assessments before deploying systems that process sensitive data, use automated decision-making, or monitor individuals at scale. The DPIA process—identifying risks, assessing controls, documenting mitigation—cannot scale without automation.
Automated Decision-Making (Article 22) restricts fully automated decision-making that produces legal effects or similarly significant impacts without meaningful human intervention. Organizations using AI for hiring, credit scoring, insurance pricing, or benefits eligibility must ensure human review and the ability for individuals to contest decisions, implement technical measures to detect and correct bias, conduct regular audits for discriminatory outcomes, and maintain transparency about decision-making logic.
Tools supporting GDPR compliance must provide:
The NIST AI RMF, while voluntary, is widely referenced across industries seeking structured approaches to AI governance. The framework organizes governance around four interdependent functions:
GOVERN establishes organizational structures, policies, and accountability for AI risk management. This includes defining risk tolerance, assigning ownership of AI systems, establishing policies for AI development and deployment, and creating cross-functional governance committees.
MAP contextualizes AI systems within their operational environment, documenting intended and potential unintended impacts. This includes identifying data sources, understanding affected populations, and documenting technical, social, and ethical considerations.
MEASURE evaluates performance and risk profile through ongoing testing, monitoring, and metrics. This involves continuous monitoring for bias, accuracy degradation, data drift, and anomalous behavior.
MANAGE implements risk mitigation strategies and adaptive controls as risks are identified or conditions change. This includes adjusting model configurations, restricting use cases, implementing additional human oversight, or retiring systems when risks exceed acceptable thresholds.
Tools supporting NIST AI RMF must provide:
The OECD Principles on Artificial Intelligence represent the first globally agreed-upon framework for trustworthy AI, adopted by over 46 countries. The five core principles—inclusive growth, respect for rule of law and human rights, transparency and explainability, robustness and security, and accountability—define aspirational standards that organizations must operationalize through concrete governance structures.
The OECD explicitly acknowledges an implementation gap: the principles define the "why" and "what," but many organizations lack systems to implement the "how." Tools supporting OECD principles must translate abstract values like "transparency" and "accountability" into concrete workflows, evidence collection, and stakeholder communication mechanisms.
ISO/IEC 42001, effective since December 2023, is increasingly referenced in procurement requirements and governance frameworks. The standard structures AI governance around Plan-Do-Check-Act methodology, requiring that all processes be documented, evidence maintained, and controls continuously monitored and improved.
Key requirements include defining organizational context and scope, ensuring leadership commitment and governance structures, identifying risks and setting objectives, allocating resources and ensuring competence, implementing governance policies and conducting impact assessments, monitoring performance and conducting audits, and implementing corrective actions for continuous improvement.
Organizations pursuing ISO 42001 certification increasingly rely on software to maintain the documentation, version control, and evidence collection that third-party auditors expect. Manual systems cannot sustain the level of rigor required for certification.
Understanding what governance tools actually do—the specific capabilities that distinguish them from general compliance software—helps organizations evaluate whether tools can address their operational needs.
The foundation of any governance program is a complete inventory of AI systems in use. Without knowing what systems exist, organizations cannot assess risk, apply controls, or demonstrate compliance.
Governance platforms maintain comprehensive catalogs including system identification and metadata (name, purpose, deployment environment, owner, business unit), model and data information (model type, framework, training data sources, dataset composition), regulatory classification (risk level under EU AI Act, applicable sectoral regulations, deployment regions), lifecycle status (development, testing, deployed, deprecated), and vendor information where applicable.
More sophisticated platforms include automated discovery capabilities that scan cloud environments, ML platforms, and data science tools to surface shadow AI systems that teams are using without formal governance. This addresses the persistent problem that organizations often cannot account for all AI systems in use, particularly when teams deploy models for experimentation or ad hoc analysis.
Governance tools systematize the process of classifying AI systems according to regulatory risk tiers and conducting documented impact assessments.
Risk classification workflows guide users through the EU AI Act's risk categories, NIST AI RMF risk profiles, or organizational custom risk models. Systems are classified based on use case, data sensitivity, populations affected, and potential impact on rights and safety. The platform maintains clear decision logic and documentation showing why each system received its classification.
Fundamental Rights Impact Assessments evaluate potential impacts on human rights and democratic values, identify vulnerable populations, and document mitigation measures. These assessments are particularly critical for high-risk systems used by public authorities.
Data Protection Impact Assessments integrate into the governance workflow, guiding users through GDPR Article 35 requirements. DPIAs are maintained as living documents that are updated when systems are retrained, when data sources change, or when new risks are identified during operation.
AI-specific impact assessments evaluate fairness, bias risk, explainability, security, and robustness. Assessment results feed into risk registers and trigger mitigation planning.
Organizations deploying AI systems in regulated domains face mandatory requirements to detect and mitigate algorithmic bias. Governance tools provide documentation and workflow support for:
Bias audits and fairness testing, either through built-in capabilities or integration with specialized bias detection tools. Results are documented, stored in version-controlled registries, and tracked over time.
Fairness metrics and thresholds help organizations define acceptable fairness standards and establish automated alerts when models exceed those thresholds. This operationalizes the principle that fairness should be monitored continuously, not assessed only at deployment.
Model cards and system documentation maintain standardized information for each model, including intended use, performance metrics, known limitations, bias assessment results, and recommendations for human oversight. Model cards serve as primary documentation for regulatory audits.
Explainability and transparency features document the logic and reasoning behind model decisions, facilitate human review of model outputs, and maintain records of when and why humans intervened to override model recommendations.
The EU AI Act requires that high-risk systems include "appropriate human oversight" and that organizations demonstrate how it operates. Governance tools systematize this through:
Human review workflow documentation records which decisions require human review, what information reviewers access, how long reviews take, and what proportion of model decisions are questioned or overridden.
Competence and training records track which personnel are responsible for oversight, what training they received, how often they review decisions, and whether their competence is validated.
Override tracking and escalation logs capture when humans override model recommendations or identify errors, logging context, reasoning, and escalation status. This supports regulatory claims that human oversight has actually prevented or corrected harmful outcomes.
Governance tools track AI systems through their full operational lifecycle:
Design and development documentation captures design decisions, training data selection, model architecture choices, and testing before deployment. This creates evidence for conformity assessments and risk management systems.
Deployment and access controls record which systems have been deployed to which environments, who has access to deploy models, what approval steps must occur, and implementation of technical controls.
Monitoring and performance tracking continuously collect performance metrics, data quality indicators, bias metrics, and operational anomalies. Dashboards show system health and alert when thresholds are exceeded.
Updating and retraining governance processes determine when and how models are retrained, including whether retraining triggers new risk assessments. Documentation of versioning enables auditors to understand model evolution and identify when retraining may have introduced new risks.
Retirement and decommissioning records when systems are deprecated, how data is handled after retirement, and what oversight was in place during transitions to alternative systems.
High-quality governance requires immutable, timestamped records of governance decisions. Governance tools provide:
Audit logs and evidence artifacts that record every governance action—risk assessment completion, approval, deployment, incident detection, remediation—with timestamp, actor, and supporting documentation. These logs are exportable in machine-readable formats for regulatory review.
Mapping to regulatory requirements allows organizations to respond to regulator requests by producing relevant assessment documents, test results, and operational logs that support specific compliance claims.
Compliance dashboards and reporting provide real-time views of governance status across all AI systems, highlighting which are compliant, which have outstanding issues, and what actions are needed.
Incident and breach tracking documents when AI systems cause harm, capturing the incident, its cause, the response, and lessons learned. This creates evidence that organizations are systematically improving governance based on real-world experience.
AI governance is increasingly inseparable from privacy governance. Organizations must maintain DPIAs, Records of Processing Activities, and consent management workflows that reflect how AI systems use personal data. Governance tools designed for AI increasingly integrate with privacy platforms or incorporate privacy capabilities including DPIA and RoPA integration, consent management for systems relying on explicit consent, and data lineage tracking which datasets feed into AI models.
Understanding where traditional compliance tools fall short helps clarify why dedicated AI governance platforms are necessary rather than optional additions to existing systems.
Traditional Governance, Risk, and Compliance platforms excel at policy management, audit workflow coordination, and cross-framework compliance mapping. However, they were built for relatively static compliance environments where controls change slowly and evidence collection happens periodically.
AI governance demands fundamentally different capabilities:
Continuous monitoring rather than periodic audits. GRC tools track whether annual audits occurred and policies were reviewed, but they don't monitor live system behavior, detect model drift, or track real-time bias metrics.
Technical integration with ML development tools. GRC platforms don't natively integrate with Databricks, SageMaker, MLflow, or Hugging Face. They can't embed governance checks into development pipelines or automatically discover models deployed without approval.
AI-specific risk assessment frameworks. Traditional GRC tools assess operational risk, financial risk, and reputational risk through general frameworks. They lack templates for fairness impact assessments, algorithmic accountability evaluations, or EU AI Act conformity documentation.
Privacy governance tools—built for GDPR compliance—share significant overlap with AI governance requirements. Both demand impact assessments, records of processing activities, and demonstration of technical and organizational measures.
However, privacy tools alone cannot address the full scope of AI governance:
Privacy tools focus on data protection and individual rights, while AI governance must also address model behavior, fairness, explainability, and safety that go beyond privacy concerns.
Privacy impact assessments (DPIAs) evaluate risks to data subjects, but AI systems create additional risks to societal groups, critical infrastructure, and democratic values that DPIAs don't capture.
Privacy tools track data processing activities, but AI governance requires tracking model versions, training processes, deployment contexts, and performance metrics across the system lifecycle.
The most effective approach unifies privacy and AI governance in integrated platforms that recognize these disciplines as interdependent rather than separate. Unified platforms provide:
Single source of truth for how personal data flows into AI systems, eliminating gaps where privacy teams track data use but lack visibility into model deployment, or AI teams build models without understanding privacy requirements.
Shared evidence repositories that link DPIAs to AI risk assessments to security vulnerability tracking, enabling rapid correlation and coordinated response to compliance issues.
Integrated workflows where privacy and AI considerations are evaluated together during system design, deployment approval, and incident response.
Reduced tool sprawl and complexity, as organizations avoid maintaining separate systems for privacy governance, AI governance, and security compliance that inevitably create fragmentation and gaps.
Selecting the right governance tool requires systematic evaluation across multiple dimensions. Organizations that skip this evaluation and choose based on vendor reputation or pricing often discover too late that the tool doesn't address their specific regulatory obligations or integrate with their technical infrastructure.
The most critical evaluation criterion is whether the tool supports the specific regulatory frameworks your organization must comply with. Create a checklist of applicable regulations:
EU AI Act compliance: Does the tool provide risk classification workflows aligned with the Act's categories? Can it generate Fundamental Rights Impact Assessments? Does it support conformity assessment documentation and EU database registration?
GDPR requirements: Does the tool integrate DPIA workflows? Can it maintain Records of Processing Activities for AI systems? Does it document automated decision-making controls and consent management?
NIST AI RMF: Does the tool organize governance around the four functions (govern, map, measure, manage)? Can it track trustworthiness characteristics and implement continuous monitoring?
ISO/IEC 42001: Does the tool support the Plan-Do-Check-Act methodology? Can it maintain the documentation and evidence collection required for third-party certification?
Sectoral regulations: If you operate in healthcare, finance, or education, does the tool address sector-specific requirements such as clinical evidence for medical AI or bias detection for lending systems?
The degree of automation determines whether the tool will scale with your AI portfolio or become a bottleneck. Evaluate:
Automated discovery: Can the tool scan cloud environments and ML platforms to surface shadow AI systems automatically, or does it require manual registration?
Risk assessment automation: Does the tool provide intelligent questionnaires that guide users through risk classification, or does it simply provide blank templates?
Continuous monitoring: Does the tool actively monitor deployed systems for drift, bias, and performance degradation, or does it only track whether periodic reviews occurred?
Evidence collection: Does the tool automatically capture audit trails, version histories, and approval records, or do teams need to manually upload documentation?
Governance tools must integrate with your existing technical infrastructure to be effective. Evaluate:
ML platform integration: Does the tool connect to your ML development platforms (Databricks, SageMaker, Vertex AI, Azure ML)? Can it pull model metadata, training data information, and deployment status automatically?
Data governance integration: Can the tool integrate with your data catalog or data governance platform to understand data lineage and quality?
Privacy platform integration: If you already use privacy management software, can the governance tool share DPIAs, RoPA entries, and consent records?
Security and identity integration: Does the tool integrate with your identity and access management systems, SIEM platforms, and security tools to create unified oversight?
API availability: Does the tool provide APIs that allow you to build custom integrations with internal systems?
Governance tools must support organizations operating across multiple business units, geographies, and regulatory jurisdictions. Evaluate:
Multi-tenant architecture: Can the tool maintain separate environments for different business units while providing consolidated oversight?
Role-based access control: Does the tool provide appropriate interfaces for technical teams, compliance officers, legal counsel, and executives, each seeing information relevant to their role?
Multi-jurisdiction support: Can the tool track different regulatory requirements across EU member states, U.S. states, and other jurisdictions, identifying conflicts and optimizing compliance?
Language support: Does the tool support the languages your teams work in across different regions?
Even organizations that select appropriate governance tools can fail if implementation is poorly executed. Understanding common failure modes helps organizations avoid repeating mistakes.
Many organizations approach AI governance as an ethics initiative focused on fairness and transparency principles. While ethical considerations matter, this framing misses the reality that AI governance is primarily a compliance and risk management problem with enforceable legal obligations.
Organizations that treat governance as ethics-only typically:
Effective implementation recognizes that AI governance is a legal compliance obligation first, requiring dedicated budget, clear accountability, and integration with existing compliance programs.
Purchasing governance software doesn't create governance—it provides infrastructure that must be operated by teams with clear roles and responsibilities. Organizations that deploy tools without establishing governance processes typically discover that:
Successful implementation establishes governance structures before deploying tools. This includes designating AI governance leads, creating cross-functional committees, defining approval workflows, and establishing escalation processes for governance exceptions.
Organizations often maintain separate compliance programs for privacy, security, and AI, each with its own tools, processes, and teams. This fragmentation creates:
Effective implementation treats privacy, security, and AI governance as integrated disciplines, ideally managed through unified platforms and coordinated governance structures.
Understanding where the market is heading helps organizations make investment decisions that remain relevant as regulations and technology evolve.
Governance software is evolving from periodic compliance assessment to continuous compliance assurance. This shift reflects regulatory expectations that governance is continuous, not episodic, and operational reality that AI systems change constantly.
Expected capabilities include automated evidence collection throughout the AI system lifecycle, real-time compliance dashboards providing current status visibility, continuous regulatory mapping that automatically updates requirements when new regulations are published, self-documenting workflows where governance actions are automatically logged with context, and proactive exception management using AI to predict compliance gaps before they become violations.
The market is moving toward unified platforms that manage privacy, security, and AI risk as integrated rather than separate disciplines. This convergence reflects regulatory and operational reality: AI systems simultaneously create privacy risks, security risks, and ethical risks. A governance failure in one dimension often cascades into failures in others.
Expected trends include unified risk frameworks assessing AI models across security, privacy, fairness, and operational risk dimensions; integrated control measures such as data anonymization serving multiple functions simultaneously; coordinated incident response protocols; and shared metadata and evidence repositories.
A significant challenge for governance is the emergence of agentic AI—systems capable of autonomous decision-making and action without real-time human intervention. Traditional governance frameworks assume human review checkpoints and predictable model behavior. Agentic systems violate both assumptions.
Governance tools will need to evolve to address agentic AI through behavioral constraint enforcement that hard-codes acceptable action spaces, multi-modal oversight mechanisms appropriate to autonomous systems, emergent behavior detection that identifies unexpected patterns, audit-ready decision logs for agent actions, and fail-safe design patterns ensuring systems revert to human control when governance exceptions are detected.
Organizations beginning their AI governance journey should follow a structured approach that builds sustainable governance rather than creating compliance theater.
Start by identifying which regulatory frameworks apply to your organization based on geography, industry, and use cases. Organizations operating in the EU must prioritize EU AI Act compliance. Those in regulated industries must address sectoral requirements. Organizations serving U.S. markets increasingly reference NIST AI RMF for structured governance.
Once you understand applicable frameworks, evaluate whether your current systems—spreadsheets, policy documents, GRC tools—can realistically operationalize those frameworks at scale. Most organizations quickly recognize that manual approaches cannot sustain the continuous monitoring, evidence collection, and documentation that regulators expect.
Select governance tools based on regulatory coverage, automation capabilities, integration with your technical infrastructure, and scalability across your organization. Prioritize platforms that unify privacy, security, and AI governance rather than creating additional silos.
Defensible governance requires more than purchasing software. Establish clear governance structures with designated AI governance leads, cross-functional committees including technical, legal, compliance, and business representatives, and defined approval workflows that embed governance into development processes.
Implement governance incrementally rather than attempting to govern all AI systems simultaneously. Start with highest-risk systems — those affecting fundamental rights or operating in regulated domains — and establish proven governance processes before scaling to lower-risk systems.
Maintain continuous improvement cycles where governance processes are regularly evaluated, evidence is reviewed for completeness and accuracy, and lessons from incidents or near-misses are incorporated into updated controls.
Most importantly, recognize that effective governance enables faster, safer AI deployment rather than slowing innovation. Organizations with mature governance demonstrate compliance more convincingly, respond to regulatory pressure more effectively, and build stakeholder trust that creates competitive advantages.
The regulatory and market imperatives are now aligned: governance operationalization through software is not an option but a market and legal requirement.
The gap between regulatory requirements and operational reality doesn't close itself. While frameworks like the EU AI Act and NIST AI RMF define what you must do, implementing these obligations across dozens or hundreds of AI systems requires purpose-built infrastructure.
Secure Privacy's AI Governance Platform translates abstract compliance requirements into machine-enforceable controls. Our unified platform helps you:
Unlike traditional GRC tools built for static compliance environments, our platform integrates with ML development workflows, provides AI-specific risk assessment templates, and maintains the continuous evidence chain regulators now demand.
Don't wait for the August 2026 EU AI Act enforcement deadline or a €35 million fine to operationalize governance.
📅 Schedule a free consultation to see how Secure Privacy transforms AI governance from policy to practice.
