White Paper

Best Security Practices for Implementing AI in the Enterprise

Deploying AI in your business creates new attack surfaces. Here's how to secure them

How to evaluate, deploy, and govern LLMs and generative AI tools without expanding your threat surface. Based on analysis of 1,500+ enterprise AI deployments and the security controls that contained real incidents.

Takes 10 seconds

今すぐダウンロード

100+

今月ダウンロードされたリソース

26%

of US workers say their organization has a generative AI policy — even though 56% are already using the tools on the job

70%

of CIOs agree that security is their top AI-related responsibility

95%

of C-level and VP leaders say privacy and protection of IP and data matter when evaluating an AI tool or feature

40%

improvement in worker performance when generative AI is used within the boundary of its capabilities

White Paper

Best Security Practices for Implementing AI in the Enterprise

AI Security Best Practices: What's Inside This White Paper

Adopting AI expands your attack surface. Every model, data store, and API you add becomes something to defend — which is why securing AI systems has to start at evaluation, not after deployment. This guide sets out the AI security best practices, controls, and governance questions security leaders need before AI reaches production.

Why securing AI can't wait

AI is already in your environment whether you approved it or not. Darktrace found 74% of active customers had employees using generative AI, while surveys show most organizations still have no formal AI policy. That gap is where data loss, shadow AI, and IP exposure happen. Reliable AI is only possible with secure AI — unsecured models malfunction, get manipulated, and leak.

How to secure AI: the three risks to control

Effective AI security guidelines protect against three failure modes:

Disruption — models malfunction, degrade, or break
Deception — prompt injection, jailbreaks, and adversarial inputs make models act against intent
Disclosure — training data, model weights, or proprietary inputs leak

A secure AI framework you can apply now

AI security controls aren't exotic. AI is just data plus models — so apply the same standards you already enforce elsewhere: extended visibility, continuous monitoring, detection and response, least-privilege access controls, zero trust, defense in depth, and vulnerability management. On top of that, AI-specific practices apply:

Build a Testing, Evaluation, Verification and Validation (TEVV) plan before launch
Red-team your models, data stores, and APIs for adversarial machine learning risk
Establish data integrity processes and guard against data poisoning and bias
Measure accuracy and require confidence scoring and explainability
Keep humans in the loop as you move from automation to augmentation to autonomy

Questions to ask every AI vendor

The white paper includes a vendor due-diligence checklist covering training data provenance, pre-trained vs. continuously learning models, retraining cadence, model access permissions, TEVV process, bias mitigation, and how your data is stored and kept private.

Download the white paper for the full secure AI adoption roadmap and vendor evaluation checklist.