Understanding the cybersecurity risks AI brings – and how to reduce them

Artificial intelligence (AI) offers great potential for innovation, but it also brings new cybersecurity risks. As AI systems gain traction, vulnerabilities can emerge at various stages of the AI lifecycle – from the data used for training to the algorithms that generate predictions. For instance, malicious actors can exploit these weaknesses by tampering with data, tricking AI models into leaking sensitive information or making incorrect predictions. To mitigate these risks, organizations must adopt a layered defense approach, including strong governance, encrypted data, secure access controls, and continuous monitoring of AI systems.

Generative AI’s rapid rise has brought about new threats to data security. AI models rely on vast amounts of data, and without proper control, these systems might inadvertently expose sensitive corporate information such as trade secrets, business strategies, or customer details. Attackers can manipulate AI through inference attacks or by feeding it faulty data, which can lead to inaccurate or biased predictions.

Mainstream AI tools like GitHub CoPilot, used in software development, can access entire codebases, which increases the risk of intellectual property theft. Without stringent security and governance, AI integration into critical business functions could lead to the exposure of proprietary data or erroneous outcomes.

A holistic approach is essential to mitigate the risks AI poses. Organizations should:

• strengthen AI security with encryption and access controls;
• educate employees on AI-related risks;
• collaborate with regulators to develop best practices.

By taking these steps, businesses can both leverage AI’s benefits and secure their valuable data.