Differences between AI Security and Traditional Security
What sets AI security apart from classic security
Traditional security focuses on protecting code, networks and systems against known, exploitable vulnerabilities through patches and perimeter controls. AI security adds a different attack surface: training data, model weights and inference behavior become targets, and threats don't always exploit a code flaw but the system's own statistical logic, as happens with adversarial examples or prompts designed to manipulate a response. In addition, many attacks on AI models are hard to detect with classic security tools because they leave no recognizable signatures, and their impact can be subtle, such as a gradually introduced bias. That's why security teams need to build machine learning knowledge and collaborate closely with data teams, something uncommon in conventional infrastructure security.