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AI Security: Risks, Regulations and ISO 42001

Sandeep Pauddar

Sandeep Pauddar, Global Program Director of IT Sector Audits, has over 25 years of work experience. His recent experience includes GRC management, internal and external audit experience, and project management.
Apr 23 , 2025

Artificial Intelligence (AI) is advancing rapidly, with machine learning (ML) models predicting outcomes, automating tasks, and generating content. But as AI systems become more capable, new risks emerge, requiring stronger security measures and regulatory oversight.

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AI’s evolution 1950 - 2025

AI has been developing since the 1950s, when Alan Turing proposed that machines could simulate intelligence. The term “artificial intelligence” was coined in 1956, leading to decades of research and breakthroughs. Historic milestones include: 

  • IBM’s Deep Blue defeating a chess champion in 1997 
  • The launch of Siri in 2011 
  • DeepMind’s AlphaGo surpassed human players in 2016.  

More recently, generative AI models like ChatGPT and DALL-E have demonstrated the ability to create human-like text and images. As a result and in combination with this development in ML and AI, new risks to companies and individuals have emerged.

AI security risks vs. traditional cybersecurity threats

Traditional cybersecurity threats involve phishing, malware, network intrusions, and data interception. These attacks often rely on human error and weaknesses in technical defenses, and, for this reason, mitigation strategies should combine network security, encryption, and user awareness training. 

AI security risks share some similarities with traditional risks but introduce new challenges: 

  • Adversarial attacks – Attackers manipulate AI models by crafting inputs that cause them to make incorrect decisions. 
  • Bias – AI models can reflect biases in their training data, leading to unfair or discriminatory outcomes. 
  • Transparency – Many AI models operate as "black boxes," making it difficult to assess how they arrive at decisions. 
  • Data poisoning – Attackers compromise AI training data, causing models to behave unpredictably. 

To provide some examples of the above: AI-powered spam filters misclassifying emails; biased AI loan approvals; autonomous vehicles making unsafe choices. Addressing these risks requires AI-specific security protocols, model validation, bias reduction, and explainability techniques. The first global entity to begin legislating around risk mitigation came into force on February 2, 2025, via the European Union (EU). 

 

AI regulation: the EU AI Act and global efforts 

The EU AI Act categorizes AI systems by risk level, imposing strict rules on high-risk applications such as healthcare and finance. It also enforces transparency requirements and penalties for non-compliance. 

Other governments are taking action: Reuters reported that China has introduced rules on generative AI, the UK has hosted AI safety discussions according to the Financial Times, and the US has issued an executive order on AI security. In response, ISO introduced the 42001 International Standard, which offers guidance for organizations to develop trustworthy AI management systems. 

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ISO 42001 for AI security and governance

ISO 42001 is an emerging standard for AI management. Similar to how ISO 27001 sets requirements for information security, ISO 42001 provides guidelines for: 

  • Accountability – Defining oversight responsibilities for AI systems. 
  • Data quality – Ensuring training data is accurate and representative. 
  • Security – Protecting AI models from attacks and misuse. 
  • Fairness and transparency – Making AI decisions explainable and reducing bias. 

Importantly, ISO 42001 and ISO 27001 assessments can be integrated. This can generate efficiencies for your organization including a significant reduction in the duration of the audits.

Implementing ISO 42001 in AI security at your company 

Organizations preparing for an AI Management System (AIMS) audit should: 

  1. Familiarize themselves with ISO 42001. 
  2. Conduct a readiness assessment. 
  3. Develop a roadmap, including an AI risk assessment. 
  4. Implement an AIMS with continuous improvement and ethical AI practices. 
  5. Engage stakeholders and integrate AI security into business operations. 

Annex B of ISO 42001 provides detailed implementation guidance, while Annex C and D cover objectives, risk analysis, and industry-specific applications. If you’re curious about what this might mean for your company, reach out to our experts today. 

 

How ISO 42001 supports EU AI Act compliance 

ISO 42001 aligns with the EU AI Act by offering structured controls for AI security and governance. A mapping document is available that links ISO 42001 clauses to EU AI Act requirements, ensuring organizations can demonstrate compliance. 

 

Business benefits of adopting ISO 42001 

Industries benefiting from AI security frameworks include: 

  • Tech companies – Ensuring ethical AI development. 
  • Healthcare – Securing AI-powered diagnostics. 
  • Finance – Strengthening AI-driven risk assessment. 
  • Retail – Improving AI-based recommendation systems. 
  • Government – Enhancing AI-driven decision-making. 

 

ISO 42001 adoption leads to: 

  • Enhanced AI security – Protecting against adversarial threats. 
  • Cost savings – Streamlining AI risk management. 
  • Regulatory readiness – Meeting compliance requirements. 
  • Competitive advantage – Demonstrating ethical AI practices. 

Challenges companies face in implementing ISO 42001

Identifying and managing AI risks.

Creating documentation for ISO 42001.

Keeping up with evolving AI regulations.

Defining KPIs for AIMS improvement.

Integrating AIMS to existing frameworks.

Monitoring AI system performance.

Why businesses should act now

AI regulations and standards are expanding. Organizations aligning with ISO 42001 will be better prepared for regulatory scrutiny and security risks. Strong AI governance also builds trust and reduces legal exposure, making it a strategic priority for any company using AI. 

Choosing DQS for your ISO 42001 certification means partnering with a trusted certification body with extensive expertise, offering comprehensive support throughout the certification process, guiding you from initial application to final certification. 

Is your company vulnerable to AI risk? Talk to our experts, with no obligation, to find out.

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Author

Sandeep Pauddar

  • Sandeep Pauddar led DQS Inc.' ANAB accreditation in ISO 17021-1 
  • ISO27001 Lead Auditor registration with PECB for ISO27001 standard
  • Data Protection Officer registration with PECB
  • PMP and ITIL certifications
  • Governance, Risk & Compliance (GRC) Professional
  • Performed GDPR/ CCPA Assessments