As organizations race to integrate AI into their products, security often lags behind — leaving critical models, data, and APIs vulnerable. Here’s a practical baseline checklist to secure your AI systems from the ground up:

1️⃣ Host & Infrastructure Security

  • Use CIS-hardened images and keep OS + dependencies patched.
  • Minimize installed software — reduce your attack surface.
  • Restrict inbound traffic and only allow HTTPS (443).
  • Enable system monitoring, auditing, backups, and endpoint protection.

2️⃣ Container Security

  • Use trusted runtimes (e.g., Docker) and keep them up to date.
  • Leverage user namespaces for isolation.
  • Continuously scan container images for vulnerabilities.

3️⃣ Network & API Protection

  • Enforce TLS 1.3 for secure data-in-transit.
  • Apply rate limiting + firewall rules between services and inference APIs.
  • Use OAuth2 + API keys for authentication.
  • Encrypt models with AES-256 and verify integrity using SHA-256.

4️⃣ Access & Identity Controls

  • Implement Role-Based Access Control (RBAC) via OS permissions or cloud IAM.
  • Follow least privilege principles to limit unnecessary access.

5️⃣ Code & Model Security

  • Apply SAST, SCA, and secret scanning in CI/CD pipelines.
  • Regularly scan Jupyter notebooks for vulnerabilities.
  • Save models safely (Keras H5, Hugging Face Safetensor) — avoid Pickle due to RCE risks.
  • Use tools like modelscan to check models for embedded threats.

Bottom line: AI security isn’t just about securing data — it’s about protecting models, infrastructure, and APIs end-to-end. Starting with these baseline controls will help reduce risks before scaling.

##💬 Over to you: How are you approaching AI security in your organization? Any tools or best practices I should add to this list? #AI #Cybersecurity #AISecurity #MachineLearning #DevSecOps #CloudSecurity