AI Security Is Different
Traditional application security focuses on SQL injection, XSS, and authentication. AI systems introduce an entirely new category of risks that most security teams aren't yet equipped to handle.
Prompt Injection
The most immediate risk. Users can craft inputs that cause the AI to ignore its instructions, leak system prompts, or perform unintended actions. If your AI system has access to tools or databases, prompt injection can lead to data exfiltration.
Mitigation: Treat all user input as untrusted. Implement input validation, use system prompts that are resistant to override, and never give AI direct access to sensitive operations without human approval.
Data Leakage Through AI
When you send data to an AI model — whether through an API or a fine-tuning dataset — that data may be used for training or stored by the provider. For regulated data (PHI, PII, financial records), this creates compliance risk.
Mitigation: Use enterprise AI agreements with data protection guarantees. Consider private deployment options like AWS Bedrock. Implement data minimization — only send the minimum context needed.
Output Trust
AI models can confidently generate incorrect information. If your system automatically acts on AI output without verification, you're trusting a probabilistic system to be deterministic.
Mitigation: Implement human-in-the-loop for high-stakes decisions. Add confidence thresholds. Log all AI decisions for audit purposes.
Model Supply Chain
If you're using open-source models or community-contributed model weights, you face supply chain risks similar to software dependencies. Models can be poisoned or backdoored.
Mitigation: Use models from trusted providers. Validate model integrity. Test model behavior against known inputs before deployment.
Access Control
Who in your organization can interact with AI systems? What data can they access through AI-powered search? AI can inadvertently become a privilege escalation vector if a junior employee can ask an AI system to query data they shouldn't access.
Mitigation: Implement role-based access control on AI systems. Ensure the AI respects the same permission boundaries as direct data access.
Start With a Threat Model
Before deploying any AI system, document: what data it can access, what actions it can take, who can interact with it, and what happens if it behaves unexpectedly. This simple exercise prevents the majority of AI security incidents we see.