A decade ago, it would have been hard to believe that artificial intelligence could do what it can do now. However, it is this same power that introduces a new attack surface that traditional security frameworks were not built to address.
As this technology becomes embedded in critical operations, companies need a multi-layered defense strategy that includes data protection, access control and constant monitoring to keep these systems safe. Five foundational practices address these risks.
1. Enforce strict access and data governance: AI systems depend on the data they are fed and the people who access them, so role-based access control is one of the best ways to limit exposure. By assigning permissions based on job function, teams can ensure only the right people can interact with and train sensitive AI models. Encryption reinforces protection. AI models and the data used to train them must be encrypted when stored and when moving between systems.
2. Defend against model-specific threats: AI models face a variety of threats that conventional security tools were not designed to catch. Prompt injection ranks as the top vulnerability in the OWASP top 10 for large language model (LLM) applications, and it happens when an attacker embeds malicious instructions inside an input to override a model’s behaviour. One of the most direct ways to block these attacks at the entry point is by deploying AI-specific firewalls that validate and sanitise inputs before they reach an LLM.
3. Maintain detailed ecosystem visibility: Modern AI environments span on-premise networks, cloud infrastructure, email systems and endpoints. When security data from each of these areas is in a separate silo, visibility gaps may emerge. Attackers move through those gaps undetected. A fragmented view of your environment makes it nearly impossible to correlate suspicious events into a coherent threat picture.
Security teams need unified visibility in every layer of their digital environment. This means breaking down information silos between network monitoring, cloud security, identity management and endpoint protection. When telemetry from all these sources feeds into a single view, analysts can connect the dots between an anomalous login, a lateral movement attempt and a data exfiltration event.