The increased use of AI in sectors such as finance, healthcare, education, marketing, and cybersecurity is simplifying and securing our lives. However, AI systems are vulnerable to cyber-attacks like prompt injection, data poisoning, and model manipulation, which can severely impact organizational data security. Business leaders are particularly concerned about report explainability, ethics, bias, and trust on the road to AI adoption. Many organizations currently lack the governance and structures needed to manage AI’s ethical challenges. This guide provides an understanding of AI adoption risks and effective mitigation strategies.
AI security involves addressing numerous risks to protect data and systems. The common risks associated with AI systems, such as compliance breach, lack of explainability, data quality and bias concerns, input manipulation, prompt injection, training data poisoning, and sensitive data disclosure, can significantly undermine the benefits of AI if not properly managed. Understanding and effectively mitigating these risks is crucial to harnessing AI’s full potential while ensuring the security and integrity of your data and systems. Download now to learn about each of these risks and explore relevant mitigation strategies in detail.
