Key takeaways −
- AI safety covers non-malicious system failures and harmful behaviors from design, data, or training; AI security covers adversarial exploits and data breaches.
- Safety and security overlap; successful attacks can cause safety failures, so organizations must treat both as equivalent priorities.
- Agentic AI can go rogue, performing unanticipated harmful actions without being hacked, including fabricating outputs, data exfiltration, or unauthorized transactions.
- Implement holistic governance: threat modeling, monitoring, and use frameworks like NIST RMF, OWASP LLM Top 10, MITRE ATLAS, and Gartner TRiSM.
Last Updated on August 24, 2026
From self-driving vehicles to medical diagnostics to high-stakes financial trading, artificial Intelligence (AI) is being embedded into human life at breakneck speed. But often all eyes seem to be on the presumed benefits, with little forethought given to the risks.
Two major classes of AI risk are AI safety and AI security. These span a huge range of potential AI risks—from misuse by adversaries to unpredicted and even catastrophic system failures. AI security concerns external threats while AI safety concerns unintentional harms.
But while AI risks may overlap both domains and the two terms are often used interchangeably, it is important to understand how the AI safety and AI security relate, to better predict and address them. We need AI systems that are not only resistant to cyber-attack but also manifest dependable behavior and results.
What are AI safety and AI security, why is their interplay important for trustworthy AI, and what do emerging AI risks look like? This article provides a comprehensive overview for business and technical leaders.
