Security Considerations

Benched.ai Editorial Team

Security considerations encompass the practices required to protect AI systems from unauthorized access, data leakage, model theft, and adversarial abuse.

Threat Surface Areas

Layer	Risk	Mitigation
API endpoint	Credential leakage, replay	TLS 1.3, HMAC request signing
Model weights	Theft of IP	Disk encryption, confidential VMs
Prompt input	Prompt injection	Input sanitation, content filters
Output	Data exfiltration via model	Post-generation moderation

Secure Development Lifecycle

Threat modeling during design.
Static and dynamic code scans in CI.
Pen-testing and red teaming pre-launch.
Continuous monitoring and patching.

Design Trade-offs

Strong encryption increases CPU cost and latency.
Strict request validation may reject legitimate traffic during migrations.
Weight obfuscation complicates debugging and observability.

Current Trends (2025)

Confidential GPU instances with SGX-like enclaves for inference¹.
Fine-grained audit logging that hashes prompts for privacy.
Model watermarking to prove ownership in theft disputes.

Implementation Tips

Rotate API keys at least every 90 days.
Use separate service accounts for training and serving.
Enable anomaly detection on token usage spikes.

Google Cloud Confidential Computing for GPUs, 2025 release notes. ↩