Challenges Posed by Unregulated or Uncontrolled AI

Let’s unpack some of the challenges or blind spots that lead to unregulated or uncontrolled AI.

Consider the issue of visibility into AI systems. When organizations lack clear insight into the deployment and operation of AI models, a phenomenon known as “shadow AI” can emerge. These unmonitored and unsanctioned models pose significant risks to security, ethics, and compliance, as they operate without proper oversight. This lack of visibility can lead to the perpetuation of biases, discrimination, and even malicious use of AI technologies.

Which models should you sanction and which ones should you block, is dependent on various risk parameters you would need to know at all times. Lack of awareness around the model risks may lead to issues like malicious use, toxicity, hallucinatory responses, bias, and discrimination.

The non-transparency surrounding the data used in AI models compounds these challenges. Lack of clarity regarding which data is being used in which AI model and which AI pipelines for training, tuning or inference may raise concerns about entitlements and the potential leakage of sensitive data. This lack of transparency not only undermines security but also raises concerns regarding compliance with data protection regulations. 

Moreover, security controls for prompts, agents, and assistants powered by AI present a significant challenge. It’s crucial to understand how the data generated by these models is being utilized – whether it’s being shared in a Slack channel, integrated into a website as a chatbot, disseminated through an API, or embedded in an app. Moreover, these agents, while serving as channels for legitimate queries, also become potential pathways for new types of attacks on AI systems.

Finally, ensuring compliance with evolving industry standards and regulations adds another layer of complexity. From the NIST AI Risk Management Framework to the laws such as the EU AI Act and many other regulations in countries like Canada, China, Brazil, and Singapore, organizations must navigate a complex regulatory landscape to ensure their AI practices align with legal and ethical requirements.

Addressing these challenges requires a multifaceted 5-step approach that encompasses enhanced visibility into AI systems, comprehensive risk assessment, transparent data practices, robust security controls, and diligent compliance with regulatory frameworks.