AI Security Best Practices: Protecting Your Business Data and Models

AI security means protecting the data you give AI systems, the models themselves and the actions AI can take. The core practices are controlling which data each AI feature can access, using enterprise model services that do not train on your data, defending against prompt injection, limiting what AI agents are allowed to do, and logging and monitoring every request.

Key takeaways

• AI adds new risks, including data leakage through prompts, prompt injection and over-privileged agents, on top of normal application security.
• Most incidents are prevented by good data governance and least-privilege access, not by AI-specific tools alone.
• Treat model inputs and outputs as untrusted, and validate them before they reach other systems.
• Write an AI usage policy so staff know which tools and data are allowed.

The main AI security risks

• Data leakage: staff paste confidential information into public AI tools, or an assistant reveals documents to users who should not see them.
• Prompt injection: hidden instructions in an email, web page or document trick the model into ignoring its rules or leaking data.
• Excessive agency: an AI agent with broad permissions takes harmful actions after being manipulated or making a mistake.
• Insecure output handling: model output is passed directly into databases, code or web pages without validation.
• Training data and model risks: poisoned training data, theft of model files or exposure of personal data used in training.
• Supply chain: unvetted plugins, open-source models or third-party AI services with weak controls.

The OWASP Top 10 for Large Language Model Applications is a useful checklist for these risks.

Best practices for protecting business data

1. Classify your data and decide which categories may be processed by which AI services.
2. Use enterprise AI services, such as Azure OpenAI or Amazon Bedrock, or private models in your own cloud, with contracts that exclude your data from training.
3. Respect existing permissions: an assistant searching documents must only retrieve what the user is already allowed to open.
4. Mask or remove personal data before it reaches a model where possible.
5. Encrypt data in transit and at rest, and keep keys in a managed key vault.

Best practices for securing AI applications and agents

• Separate system instructions from user content, and treat retrieved documents as untrusted input.
• Filter inputs and outputs for sensitive data and policy violations.
• Give agents the minimum permissions they need, through dedicated accounts, and require human approval for high-impact actions.
• Validate every tool call and every output before it is used by another system.
• Rate-limit requests and set spending limits to prevent abuse and runaway costs.
• Red-team the application with realistic attack prompts before launch and after major changes.

Monitoring and governance

Log prompts, responses and tool calls, with appropriate privacy controls, so incidents can be investigated. Monitor for unusual usage patterns, track model versions, and review access regularly. Governance should also cover which AI tools staff may use, how new use cases are approved and who is accountable for each AI system.

A practical starting checklist

1. Publish an AI acceptable-use policy for staff.
2. Provide an approved, enterprise AI tool so staff do not turn to public ones.
3. Inventory existing AI use across the organisation.
4. Apply least-privilege access and logging to every AI integration.
5. Test new AI applications against prompt injection before launch.

Security is built into every project we deliver, from enterprise AI integration to generative AI development. Our cybersecurity industry solutions cover AI for threat detection and secure LLM systems, and our guide to AI agents explains how to give agents safe guardrails.

Want to adopt AI without putting your data at risk? Tell us about your plans, and we will suggest the controls you need. Start your AI journey.

Last updated: October 2026 · Prominent AI Services LLP engineering team