AI Security Blueprint - Tech & SaaS Edition

Overview

Platform AI security operates at a different scale and faces different threats than enterprise AI. Your models are trained on proprietary data worth billions. Your APIs handle millions of requests from potentially adversarial users. Your LLMs must resist prompt injection while remaining useful. Your infrastructure must stay secure while supporting rapid iteration.

This comprehensive security blueprint is built for tech and SaaS companies operating AI at platform scale. It provides security architecture, controls, and operational practices that work at scale—protecting models, data, APIs, and infrastructure while enabling the rapid development that platforms require.

What's Inside

• Tech AI Threat Landscape: Platform-specific threat analysis covering model theft, training data extraction, adversarial attacks on production systems, coordinated manipulation campaigns, and supply chain attacks on AI dependencies
• Security Architecture: Reference architecture for secure AI platforms including network segmentation, zero-trust implementation, secrets management, and secure CI/CD for ML pipelines
• Model Security: Comprehensive controls for protecting models including access controls for training infrastructure, model artifact protection, watermarking, and theft detection
• Data Protection: Security architecture for training data including encryption, access controls, data lineage tracking, and secure data pipelines
• API Security: Securing AI APIs at scale including authentication, rate limiting, input validation, output filtering, and abuse detection
• LLM & Generative AI Security: Specific controls for large language models including prompt injection defense, jailbreak prevention, output safety filtering, and responsible AI guardrails
• Infrastructure Security: Platform infrastructure hardening including GPU cluster security, model serving infrastructure, feature stores, and vector databases
• Incident Response: AI-specific incident response procedures including model compromise, training data breach, adversarial attack detection, and coordinated manipulation response

Who This Is For

• Chief Information Security Officers at platform companies
• Security Engineering Leaders building AI security infrastructure
• ML Platform Teams operating training and serving infrastructure
• Trust & Safety Engineering securing content moderation and safety AI
• Application Security teams reviewing AI-powered features

Why This Resource

Generic security frameworks don't address AI-specific threats. This blueprint covers model theft and extraction, training data poisoning, adversarial inputs designed to fool models, prompt injection in LLMs, and the unique challenges of securing infrastructure that operates at billions of inferences per day.

Every control is designed for platform operations: automated security testing in ML pipelines, runtime monitoring at scale, and incident response that accounts for the speed at which AI security events can impact users.

FAQ

Q: How do we secure LLMs against prompt injection?

A: The LLM security section covers defense-in-depth for prompt injection: input sanitization, system prompt protection, output filtering, anomaly detection, and architectural patterns that separate trusted and untrusted content. It also covers jailbreak detection and response.

Q: What about securing AI APIs at scale?

A: API security covers authentication patterns for AI endpoints, rate limiting strategies, input validation for ML inputs (which differ from traditional API inputs), output filtering, abuse detection at scale, and cost protection against denial-of-wallet attacks.

Q: Does this cover ML pipeline security?

A: Yes. Infrastructure security includes secure CI/CD for ML pipelines, training infrastructure access controls, model artifact protection, and the security of supporting infrastructure like feature stores and vector databases.