InfoSec Made Easy

Generative AI is moving faster than most organizational control structures. Employees are already using tools like ChatGPT, Copilot, Claude, and image generators to write code, summarize documents, build presentations, and analyze data—often without security or legal review. Banning generative AI outright is rarely effective. Ignoring it is worse. What organizations need is a clear, enforceable Generative AI policy that: Enables productivity Protects sensitive data Manages legal, ethical, and security risk Aligns with a recognized framework The NIST AI Risk Management Framework (AI RMF) provides a strong foundation for doing exactly that. Why Generative AI Policies Matter Generative AI introduces new risk categories that traditional IT or acceptable-use policies do not fully address: Data leakage through prompts and outputs Model hallucinations treated as fact Intellectual property exposure Bias and ethical risk Shadow AI adoption Regulatory and compliance gaps A well-designed pol...

Generative AI has moved faster than nearly any technology security leaders have dealt with. Tools that can generate text, code, images, and data insights are now embedded into productivity platforms, security tooling, development workflows, and business operations—often before security teams are formally involved. For CISOs, this creates a familiar but amplified challenge: innovation is happening faster than governance, and unmanaged generative AI introduces material risk across confidentiality, integrity, availability, compliance, and trust. For aspiring information security professionals, AI governance represents a growing and valuable discipline where strategic thinking matters just as much as technical depth. The good news? We don’t need to invent governance from scratch. NIST’s AI Risk Management Framework (AI RMF) provides a practical, flexible structure that security leaders can use today to govern generative AI responsibly and defensibly. Why Generative AI Governance Matt...