Blog

In banking, ethical generative AI use means preventing unfair treatment, protecting customer and supervisory data, avoiding IP misuse, reducing misinformation/deepfake harms, and ensuring clear accountability with audit trails. Approve GenAI only when it is governed, tested, monitored, and traceable to […]

Teams using generative AI should set rules that reduce harm and liability across fairness (bias), privacy/security, intellectual property, accuracy/misinformation, security/misuse, and accountability/transparency. Start by restricting sensitive inputs, verifying high-impact outputs, disclosing material AI involvement, and assigning a human owner for […]

Generative AI escalates cybersecurity risks by scaling social engineering and introducing application-layer vulnerabilities like prompt injection and data leakage in LLM apps. Mitigate these threats by defining data boundaries, enforcing least privilege for tools, and hardening output handling to prevent […]