The Role of Standardisation in Managing AI Risks
As AI reshapes industries globally, organisations face a multitude of risk management challenges. This technological transformation compels not only companies but also regulators and governments to formulate AI governance frameworks tailored to their specific risks and concerns.
For instance, the OECD AI Policy Observatory tracks over 1,000 AI policy initiatives from 69 countries and the EU, showcasing the varied approaches to regulatory reach concerning AI risks.
Despite regulatory measures, the inevitability of AI risks necessitates a standardised approach that incorporates global consensus, guiding organisations in balancing innovation with effective risk management.
The AI Risk Matrix: Why It’s Not All New
AI shares numerous risk management practices with traditional software, including development cycles and technology stack hosting. However, the unpredictable nature of AI and its reliance on data introduces unique risks alongside existing technology risks.
Firstly, the rise of generative AI has led to broader adoption, increasing the attack surface and exposure to risks. Secondly, as generative AI models utilize more enterprise data, the potential for accidental disclosure of sensitive information rises, particularly in cases where access controls are inadequately implemented. Thirdly, AI poses challenges in areas such as privacy, fairness, explainability, and transparency.
Finding Balance in a Time of Constant Change
The rapid evolution of AI presents significant challenges, especially as risk management evolves alongside it. Organisations face a dilemma: lagging in AI adoption risks losing competitive advantage, while rushing could lead to ethical, legal, and operational pitfalls.
Striking the right balance is crucial, affecting not only large corporations but also smaller firms across various industries as they integrate AI into their core operations. The question remains: how can organisations manage risks effectively without hindering innovation or imposing overly stringent requirements?
This is where efforts towards standardisation come into play, such as the ISO/IEC 42001:2023, which offers guidance for establishing, implementing, maintaining, and improving an Artificial Intelligence Management System (AIMS). Developed by the ISO/IEC JTC 1/SC 42 subcommittee for AI standards, this framework represents a global consensus, providing a structured approach for managing risks associated with AI deployment.
Rather than tying itself to specific technological implementations, the guidance emphasizes establishing a strong tone from the top and implementing a continuous risk assessment and improvement process. This aligns with the Plan-Do-Check-Act model, fostering iterative risk management rather than one-time compliance. It equips organisations with the necessary components to manage risks proportional to their scale and complexity.
As a certifiable standard, ISO/IEC 42001:2023 is also verifiable. Organisations can pursue formal certification or adhere to it as best practice. In either scenario, demonstrating compliance helps organisations convey their commitment to managing AI-related risks to stakeholders.
Standardisation: The AI Pain Panacea
Adhering to a standard like ISO 42001 offers additional benefits. Its framework addresses the fragmentation of AI adoption within organisations, which has often been isolated within data science teams. The widespread adoption of generative AI has led to an implementation sprawl, necessitating more robust management of AI risks.
This brings forth three significant pain points: the unclear accountability regarding AI decisions, the difficulty of balancing speed with caution, and the challenges faced by firms operating across jurisdictions in navigating fragmented regulatory guidance.
Once again, a standardised approach proves effective. ISO 42001’s internationally recognised framework for AI governance establishes clear accountability structures and focuses on guiding principles rather than dictating specific technologies or compliance steps. This principles-based approach mitigates two primary concerns surrounding AI risk management: the potential to stifle innovation and the risk of overly prescriptive standards becoming obsolete quickly.
In a world where AI is increasingly integrated into business operations, organisations must be proactive in preparing for its associated risks. By standardising their approaches, they position themselves to navigate future AI regulations more seamlessly, mitigate compliance risks, and innovate responsibly. In doing so, AI can continue to serve as a force for good for both organisations and society at large.
