AI applications introduce unique security considerations that traditional software development checklists do not cover. Here is what you need to address.
Data Input Validation
AI models can be manipulated through carefully crafted inputs. Implement strict input validation, rate limiting, and content filtering before data reaches your models. Treat all user input as potentially adversarial.
Model Access Controls
Apply the principle of least privilege to model access. Not every service or user needs access to every model capability. Segment access based on role and use case.
Output Sanitization
AI-generated content should be treated with the same caution as user-generated content. Sanitize outputs before rendering, especially when outputs might contain code, HTML, or structured data.
Data Privacy in Training
If your AI system learns from customer data, ensure clear consent, data anonymization, and the ability to remove specific data on request. Document your data handling practices transparently.
Audit Logging
Log all AI interactions comprehensively: inputs, outputs, model versions, and decision metadata. This supports debugging, compliance, and the ability to investigate incidents.
Human Oversight
Maintain human oversight for high-stakes decisions. AI should augment human judgment, not replace it for decisions with significant consequences. Implement review workflows for critical operations.