Artificial intelligence (AI) has revolutionised our world, offering unprecedented possibilities and transformational advancements across industries. Yet, amidst this exhilaration lies a profound paradox – the very technology designed to enhance human lives poses significant risks that must not be ignored. In this blog post, we will delve into the enigma of AI risk, examining the dual nature of AI and exploring ways to navigate through this paradox.
Risk
Before we delve into AI risk, a good starting point would be to understand risk and quantify it.
Definition:
Risk is the possibility of something unintended or negative happening or the chance of loss or harm . Here, the emphasis is on the terms possibility/ chance and loss/harm.
Quantification:
Risk = Probability of a negative outcome * Loss or harm from the occurrence of the outcome.
AI Risk
Now, that we understand risk and are able to quantify it, AI Risk follows suit. However, the question we need to ask is in the world of AI and especially GenAI, what are these negative outcomes?
Adoption of GenAI opens up new vectors of attack, abuse and compliance risk. And these vectors are beyond the reach of conventional risk management measures. For example, protection against threats such as injection of a malicious prompt into an AI powered chatbot was out of scope for our age old security measures. Like wise, data poisoning or data exfiltration through AI systems are problems of the new age. Hence, adoption of AI has opened up new doors for attack.
Similarly, on the compliance front, we have evolving global requirements and regulations ensuring the responsible adoption of AI. And non-compliance to these can pose huge risk in terms of cost and business continuity.
These attacks and events of abuse and non-compliance are the potential negative outcomes in the era of GenAI.
However, what’s noteworthy here is that while the probability of these negative outcomes happening can be low-medium, the loss or harm from their occurrence is high-very high. The impact can range from profit erosion to complete damage to the brand and hence is something which cannot be left to chance.
Embracing the paradox
To fully harness the power of AI without succumbing to its risks, we must confront the paradox head-on. We need to pre-empt spotting of the vulnerabilities which surface alongside the adoption of AI and have real time measures in place to deflect such negative outcomes. The question then arises that what are the best practices builders and CXOs need to keep in mind as their organisations adopt GenAI and leverage it across business uses cases.
While AI risk vectors are still evolving, it is wise to account for the ones which already show meaningful probability of occurrence and have control measures in place such as: For example:
Comprehensive and dynamic vulnerability identification: Identifying vulnerabilities in your AI systems along the development lifecycle which make them prone to attacks and abuse and fine tuning the same
Real-time intent based protection: Having a real time protection layer in place to deflect such attacks and abuse attempt
Compliance: Adherence to regulatory and compliance standards evolving in your key geographies of operation
Internal Governance: Complete shadowing of the AI systems used across the organisation and ensuring the right access controls
AI risk mitigation
Given these vectors of attack are over and above the ones from the pre-GenAI era, we need to see AI risk management as an additional body of work. Manual assessments may prove sluggish and costly when identifying flaws within enterprise AI systems. Automated solutions, however, can convert static risk management approaches into dynamic, preventive actions. With these automated tools, businesses can actively monitor and reduce risks, thereby fostering greater assurance in scaling up their AI applications confidently.
Conclusion
As we continue to embrace AI's potential, it becomes increasingly important to acknowledge and address these associated risks. And instead of shying from the wide scale adoption of this immensely useful technology, enterprises should focus on having a robust and holistic AI risk management system in place which augments their conventional security and risk mitigation measures.
