The EU AI Act: What CISOs Need to Know About the World’s First AI Regulation 

Artificial intelligence is no longer experimental—it’s embedded in critical business functions. From fraud detection and customer engagement to cybersecurity operations and predictive decision-making, AI systems are becoming core to how organizations operate and compete. But with rapid adoption, increasing scrutiny comes. Regulators are moving quickly to ensure that AI is used responsibly, ethically, and safely. 

The EU AI Act, finalized in 2024, represents the world’s first comprehensive law designed specifically to govern AI. Its impact will stretch far beyond Europe’s borders. Just as GDPR reshaped global privacy practices, the AI Act is expected to influence regulatory standards worldwide. For organizations of every size, it signals an urgent need to strengthen governance, compliance, and risk management strategies around AI. 

What is the EU AI Act? 

At its core, the EU AI Act is about building trust in AI systems. The regulation introduces clear requirements for how AI is developed, deployed, and monitored. Its key objectives include: 

• Protecting individuals from harmful or biased AI that could infringe on fundamental rights. 
• Increasing transparency, so organizations can demonstrate how AI systems reach their outcomes. 
• Strengthening accountability for companies that deploy high-impact AI across sensitive industries. 

Like GDPR, the AI Act has extraterritorial reach. This means companies based outside the EU are still covered if their AI systems impact EU citizens. Enforcement began gradually in 2025, with many deadlines for compliance in 2026 and 2027. Organizations are encouraged to look at the full timeline if concerned about the impact on your business

(historical and current timeline available at https://artificialintelligenceact.eu/implementation-timeline/).  

A parallel can be drawn to GDPR: when enforcement began, many organizations scrambled to catch up, leading to costly retrofits of privacy programs. The EU AI Act provides a similar warning—those who prepare early will avoid disruption and strengthen their competitive position. 

A crucial detail is the system classifications. Most of the act is focused on requirements for high-risk AI systems, which should be highly regulated. A smaller scope of requirements speaks to limited risk AI systems, which means these are subject to lighter transparency obligations. 

Preparing for AI Regulation 

Organizations should not wait until enforcement begins. Early action reduces compliance costs, avoids rushed fixes, and creates a foundation for long-term resilience. A proactive strategy includes: 

1. Catalog AI Systems 

Identify all AI systems in use, whether developed internally, purchased from vendors, or integrated into cloud services. Many organizations underestimate how pervasive AI already is in their environment. Identify how AI systems interact with the rest of your corporate environment to determine whether sensitive data is processed, or critical business functions are dependent on the AI system. Furthermore, identify how the AI behaves. This is critical for compliance with Article 5 of the Act which details prohibited activities of AI systems, such as subliminal or manipulative methods for gathering information.  

2. Classify Risk Exposure 

A key component of the Act surrounds high-risk AI systems. Sections 1 and 2 give details on classification and requirements for high-risk AI systems, respectively. Map AI use cases against the Act’s risk tiers. For example, a fraud detection model used in financial services is likely “high risk,” while an internal HR chatbot may fall under “limited risk.” Early classification will help organizations understand where to focus on compliance investments. Classify an AI system as a similar information asset: its risk score is based on the level of confidential or sensitive data in-scope as well as its effect on the organization in the event of downtime.   

3. Define Accountability 

AI does not fall neatly into a single department. Assigning ownership across legal, risk, compliance, and technology functions is essential. Governance bodies, such as an AI oversight committee, are increasingly seen as best practice. 

4. Document and Monitor 

Documentation is central to compliance. This includes explainability of models, audit trails of decisions, and records of testing. Detail how users consent to the usage of AI and what level of transparency is maintained to ensure the AI is functioning in line with customer agreements. Ongoing monitoring is critical to catch model drift, bias, or unintended behaviors over time. 

5. Strengthen Vendor Oversight 

Third-party management is a key factor of the AI Act. Section 3 defines the requirements of various parties in the AI lifecycle, such as AI providers and importers. Third-party providers often supply AI capabilities embedded into enterprise applications. Organizations must extend their vendor risk management programs to include AI-specific controls and contractual obligations. 

By embedding these steps into existing enterprise risk management and compliance frameworks, organizations can avoid duplicating efforts and treat AI oversight as part of their broader governance ecosystem. 

6. Quality Management System 

Article 17 of the AI Act details the need to have a Quality Management System (QMS). This is defined as documenting policies and procedures detailing how the quality of the system is maintained. This includes detailing how the AI system meets any in-scope regulatory requirements, documenting design plans, detailing testing methodology, maintaining standards for record keeping, and creating and using channels to communicate with customers. For organizations with no QMS process currently, Tevora would recommend looking into ISO 9001 for detailed instructions on how a QMS can be established. 

A Risk-Based Framework for AI 

The EU AI Act takes a risk-based approach, a familiar concept for compliance and security leaders. Instead of applying a one-size-fits-all rulebook, the regulation categorizes AI based on potential harm: 

• Unacceptable risk – AI systems that clearly endanger safety or rights (e.g., manipulative applications, government-run social scoring) are banned outright. 
• High risk – AI used in sensitive areas like healthcare, finance, critical infrastructure, and HR must meet stringent requirements, including transparency, human oversight, and risk management controls. 
• Limited risk – Certain systems, such as chatbots, require disclosure to ensure users know they are engaging with AI. 
• Minimal risk – Low-impact AI, like spam filters or recommendation engines, has minimal compliance requirements. 

This structure reflects established principles of proportionality in risk management: higher-risk systems demand stronger controls. For organizations already working with frameworks such as ISO 27001 or NIST CSF, the EU AI Act’s approach should feel familiar. 

Why Building AI Governance Programs Now Matters 

While the EU is first to pass binding AI regulation, it will not be the last. California is already exploring AI-specific legislation, and U.S. states often act as testing grounds before federal policy takes shape. Similarly, Canada, the UK, and several countries in Asia are actively developing AI governance frameworks. 

Much like GDPR catalyzed global privacy regulation, the EU AI Act is likely to accelerate AI laws across jurisdictions. Organizations should view compliance not as a one-off project, but as part of a long-term AI governance program that scales across borders. 

Key components of effective AI governance include: 

• Policies and Standards – Defining how AI is designed, procured, deployed, and monitored responsibly. Documenting the standards needed to maintain the quality of the system as defined by the Quality Management System requirement of Article 17. 
• Risk Management Frameworks – Leveraging global standards like the NIST AI Risk Management Framework (AI RMF), which provides guidance for identifying, assessing, and mitigating AI risks, or ISO/IEC 42001, the first international standard for AI management systems. 
• Executive and Board-Level Reporting – Elevating AI risk alongside cybersecurity, privacy, and compliance in board discussions. 
• Continuous Monitoring – Establishing feedback loops to evaluate model performance, detect bias, and identify emerging vulnerabilities. 

Aligning with globally recognized frameworks, organizations not only accelerate maturity but also provide defensibility when regulators or stakeholders ask how AI risks are being managed. 

The Strategic Benefits of Proactive Compliance 

While the EU AI Act is a regulatory requirement, it also presents an opportunity for organizations to gain competitive advantage. Benefits of building governance early include: 

• Stronger customer trust – Demonstrating transparency and accountability strengthens brand reputation. 
• Improved operational resilience – Risk assessments and monitoring reduce exposure to AI-related failures or incidents. 
• Better vendor relationships – Clear expectations for third parties improve overall supply chain security and compliance. 
• Board and investor confidence – Governance demonstrates forward-looking risk management, a priority for stakeholders increasingly aware of AI’s potential downsides. 

In other words, compliance and competitiveness go hand in hand. Those who prepare early are more likely to differentiate themselves in crowded markets. 

The Bottom Line 

The EU AI Act marks a turning point in the governance of artificial intelligence. It makes clear that AI systems must be safe, transparent, and accountable. For organizations, it signals that AI oversight can no longer be treated as optional or reactive; it should be embedded into the enterprise’s risk management strategy. 

By acting now to catalog AI systems, assess risks, align with global frameworks, and establish governance programs, businesses will not only meet upcoming compliance obligations but also strengthen resilience and trust in an AI-driven economy. 

The lesson from GDPR was clear: waiting until regulators come knocking is the most expensive option. The EU AI Act gives organizations a window of opportunity to prepare. Those who take it will lead the way in building AI systems that are both innovative and trustworthy.