AI systems are becoming a priority—they’re critical for tasks like fraud detection, predictive maintenance, and personalised customer experiences. But with their benefits come risks that must be addressed early to avoid serious issues. This is why an AI risk management framework is essential. A key component of this framework is the AI risk profile, which evaluates six critical pillars: Safety, Security, Legal, Ethics, Performance, and Sustainability. By identifying the potential threats and associated risks, organisations can effectively prioritise their mitigation efforts, ensuring AI systems remain compliant with the regulations, aligned with organisational goals and achieve the business objectives with confidence. This blog delves into these six pillars.
Six Key Areas of an AI Risk Profile
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Safety
Does the AI system have the potential to harm anyone or anything—either directly or indirectly? Safety risk identification is essential to prevent physical harm or detrimental outcomes. For instance, Tesla is under investigation regarding safety concerns with its Autopilot feature, following reports of hundreds of collisions and 13 fatalities linked to its use. Ensuring robust safety mechanisms in AI systems is critical to avoid such risks[1].
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Security
Can the AI system resist cyber threats and AI-specific attacks while safeguarding sensitive data? AI systems face unique security challenges, such as susceptibility to adversarial attacks that can manipulate outputs or compromise confidential data. For instance, Slack AI can be tricked into leaking sensitive data from private channels through sophisticated prompt injection techniques, enabling attackers to manipulate the AI system into revealing confidential information without direct channel access[2].
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Legal
Is the AI system compliant with laws and regulations? Non-compliance can lead to severe penalties. In 2022, Clearview AI was fined £7.5 million by the UK’s Information Commissioner’s Office for collecting facial recognition data without consent, underscoring the financial and reputational risks of violating privacy laws[3].
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Ethics
Does the AI system uphold fairness, transparency, and respect for stakeholders? Ethical breaches can erode trust and cause harm. For example, iTutorGroup’s recruiting AI was found to discriminate against older applicants, resulting in a $365,000 fine and significant reputational damage to the company. This highlights the consequences of unchecked bias in algorithms[4].
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Performance
Does the AI system deliver on its intended purpose without compromising outcomes? Ensuring performance reliability reduces investment risks and maximises returns. Poorly performing systems not only waste resources but also undermine confidence in AI adoption. Several cities in the U.S. have decided to not renew ShotSpotter, a gun detection technology after tens of millions spent, due to cost and effectiveness concerns.[5]
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Sustainability
Has the AI system been developed with sustainability considerations in mind? This is not only about environmental sustainability but also the efficient and responsible use of organisational resources, ensuring long-term viability by balancing financial, operational and environmental factors. Complex AI models often demand substantial energy, impacting sustainability. Microsoft’s commitment to carbon-neutral AI operations demonstrates how organisations can align innovation with environmental responsibility[6].
Conclusion
AI risk management is pivotal to deploying responsible, future-proof systems. By building a risk profile across six critical pillars—Safety, Security, Legal, Ethics, Performance, and Sustainability—organisations can identify the potential threats and associated risks and address them effectively. This foundation enables targeted mitigation, enhancing compliance and ensuring the ethical use of AI. Proactive risk management is not just a safeguard; it is a strategic enabler for scaling AI responsibly.
As an AI risk and quality management platform provider, AIQURIS ® empowers organisations to adopt and scale AI with confidence—each deployment is supported by a clear, structured risk profile across six key areas. If you're planning to integrate an AI solution into your project or use case, consult an expert to learn how to conduct risk profiling for your AI initiatives.
