Zero Trust and AI: a symbiotic 8 relationship for end-to-end security Verify explicitly Use least Assume breach Protect assets against privilege access Assume attackers attacker control by Limit access of can and will explicitly validating a potentially successfully attack that all trust and compromised asset, anything (identity, security decisions typically with just-in- network, device, app, use all relevant time and just-enough- infrastructure, etc.) and available information access (JIT/JEA) and plan accordingly. and telemetry. risk-based policies like adaptive access control. Business enablement: Align security to the organization’s mission, priorities, risks, and processes. Considering Zero Trust to help secure AI Classic network-perimeter security • AI activity is dynamic, so it doesn’t approaches simply cannot protect data or AI match static patterns. These controls are applications. Firewalls, IDS/IPS, and Network designed to detect and block. Additionally, DLP controls in a network perimeter focus AI also allows rapid generation of new on detecting and mitigating risk using static tooling that can copy existing functionality predictable patterns of network traffic. These (e.g. a custom NMAP clone) which can classic approaches don’t work effectively for evade static signature-based defenses. AI applications because: Additionally, one way to protect data • AI network traffic is often encrypted effectively is with a data-centric approach that for privacy and security reasons, which stays with data wherever it goes. Network prevents these network-based controls controls are limited to the organization’s from getting any visibility. security perimeter and the assets in it so they cannot protect data on mobile devices, cloud • AI operates at data and application services, USB drives, and other locations. For abstraction layers, so differentiating these reasons, a Zero Trust asset-centric and between dangerous and safe actions/ data-centric approach is an effective way to communications requires controls that protect AI and related data assets. understand applications, data, and users.