Zero Trust: a proven solution for the new AI security challenge (www.techradar.com)

As enterprises race to adopt LLMs and autonomous agents, this TechRadar Pro piece argues that the established Zero Trust security model — "never trust, always verify" — is the most practical way to manage the new AI risk surface. The core claim: LLMs and agents magnify traditional data-leak and misuse pathways because they operate at machine speed, can chain API calls across systems, and are vulnerable to prompt-injection or logic-jailbreaks. Rather than relying on brittle prompt/output filtering, organizations should treat AI components like users: assign identities, roles, and short-lived entitlements so every request is authenticated and authorized in real time. Technically, the article recommends extending Zero Trust controls down the stack: fine-grained, context-aware access policies (time, device, data sensitivity), protocol- and network-level enforcement, per-agent identity and traceable entitlement propagation across multi-step agent/model workflows, and continuous monitoring with tamperproof logs and session recording. Practically, this requires IAM for models/agents, least-privilege entitlements, and enforcement points that prevent a compromised prompt from arbitrarily escalating access. The payoff is dual: materially lower large-scale exfiltration risk and a clearer path to regulatory compliance, enabling safer, faster AI deployment without sacrificing innovation.