Balancing innovation and security
There is so much incredible promise in AI right now but also incredible peril. Users and enterprises need to trust that the AI dream won’t become a security nightmare. As I’ve noted, we often sideline security in the rush to innovate. We can’t do that with AI. The cost of getting it wrong is colossally high.
The good news is that practical solutions are emerging. Oso’s permissions model for AI is one such solution, turning the theory of “least privilege” into actionable reality for LLM apps. By baking authorization into the DNA of AI systems, we can prevent many of the worst-case scenarios, like an AI that cheerfully serves up private customer data to a stranger.
Of course, Oso isn’t the only player. Pieces of the puzzle come from the broader ecosystem, from LangChain to guardrail libraries to LLM security testing tools. Developers should take a holistic view: Use prompt hygiene, limit the AI’s capabilities, monitor its outputs, and enforce tight authorization on data and actions. The agentic nature of LLMs means they’ll always have some unpredictability, but with layered defenses we can reduce that risk to an acceptable level.
