AI is the most powerful technology that has ever been built. That sentence applies equally to the opportunities it creates and the risks it introduces. When your AI feature can take actions — sending emails, updating records, accessing customer data, making decisions — it needs a security model before launch. Not after. Not "when we have time." Before.
This checklist is for leaders who are moving fast with AI and want to make sure they do not move recklessly. It covers the security requirements for taking an AI feature from prototype to production, organized into pre-launch, infrastructure, and post-launch controls.
Before writing a line of production code, map the abuse paths. For every AI feature, ask:
This is not theoretical. These attacks are happening today. If your AI feature can access a database, someone will try to get it to dump that database. Plan for it.
The AI should have access to the minimum data and permissions required to perform its function. Nothing more. If the AI helps with customer support, it should not have access to financial records. If it generates reports, it should have read-only access — never write access to production databases.
Use scoped credentials. Create service accounts with specific, limited permissions. Rotate them regularly. Never give an AI feature your admin credentials "because it's easier."
Input validation: Every piece of data that flows into the AI — user prompts, uploaded files, API payloads — must be sanitized. This means stripping injection attempts, validating formats, enforcing size limits, and rejecting unexpected input types.
Output validation: Everything the AI produces must be validated before it reaches the user or triggers an action. Check for data leakage in responses (is the AI including information the user should not see?). Validate structured outputs against expected schemas. Filter responses for PII, credentials, or internal system information.
Tool and function call validation: If the AI can call functions or tools, validate every call against an allowlist. Ensure parameters are within expected ranges. Log every tool invocation for audit purposes.
Prompt injection is the most significant AI-specific security risk. It occurs when a user crafts input that overrides or manipulates the AI's instructions. Defenses include:
Every AI action must be logged. The log should include:
These logs must be tamper-resistant — meaning they cannot be modified or deleted by the application itself. Use append-only logging services or write-once storage. When something goes wrong — and eventually something will — this audit trail is how you understand what happened and how you fix it.
Before launch, dedicate time to actively trying to break the feature. This means:
One powerful technique: ask Claude to review the code as if a third-party security researcher were trying to break it. AI is excellent at finding its own vulnerabilities when asked directly.
Standard application security still applies. Validate API authentication, session management, and authorization boundaries. Test for SQL injection, XSS, CSRF, and other web application vulnerabilities. The AI layer is an additional attack surface — it does not replace the existing ones.
When an application leaves your computer and goes into the cloud, you need:
If your AI feature touches protected health information (PHI), the requirements are stricter:
Similar principles apply to PCI-DSS for payment data, SOC 2 for SaaS products, and GDPR for European user data. The AI feature must comply with whatever regulatory framework governs your data.
Set rate limits on AI feature usage per user and per session. Monitor for anomalous patterns — sudden spikes in usage, unusual query patterns, or attempts to access data outside normal scope. Alert on anomalies immediately.
Every AI feature that can take autonomous actions must have a kill switch — a way to immediately disable the feature without taking down the entire application. When you discover a vulnerability or an abuse pattern, you need to be able to shut it down in minutes, not hours.
Schedule a weekly review of AI feature logs, anomaly reports, and user feedback. Look for patterns that suggest abuse, unexpected behavior, or security gaps. AI features evolve differently from traditional software — the same feature can behave differently based on the inputs it receives, so continuous monitoring matters more than point-in-time testing.
When the underlying AI model is updated — which happens frequently — retest the feature. Model updates can change behavior in subtle ways that affect security. A prompt injection defense that worked with one model version might not work with the next.
The goal is not to slow you down. The goal is to make speed sustainable. Fast prototyping is easy now. Safe deployment still matters. The companies that will win are the ones that move fast and maintain security discipline — not the ones that sacrifice one for the other.
Be very careful with production access to data, credentials, customer information, permissions, and code touching live systems. Before you go from local to production, bring in someone who knows what they are doing to review it. This is not optional.
We help leaders ship AI features with proper security controls, threat modeling, and production infrastructure.
Apply for a Strategy Call