AI Security Blueprint - Public Overview

Overview

Every week brings new headlines about AI security failures: chatbots hijacked to produce harmful content, vision systems fooled by invisible perturbations, models leaking training data, malicious models uploaded to public repositories. These aren't hypothetical risks—they're happening now, and traditional security tools can't detect or prevent them.

This free overview introduces AI security fundamentals. It explains what makes AI security different from traditional cybersecurity, maps the threat landscape specific to AI systems, and introduces the defensive framework you need to protect your AI investments.

What's Inside

• Why AI Security Is Different: The gap between traditional cybersecurity and AI-specific threats—what conventional security tools miss and why AI requires specialized defenses
• Real-World Incidents: Case studies of AI security failures including Microsoft's Tay chatbot hijacking, Tesla Autopilot adversarial attacks, GPT jailbreaks, and supply chain compromises
• The AI Threat Landscape:
  • Input Attacks: Adversarial examples, prompt injection, data poisoning
  • Model Attacks: Model extraction/theft, model inversion, membership inference
  • Output Attacks: Training data leakage, hallucination exploitation
  • Supply Chain Attacks: Malicious pretrained models, compromised datasets, dependency vulnerabilities
• The 5 Pillars of AI Security: Framework overview covering secure development lifecycle, model security, data protection, infrastructure security, and monitoring/response
• Getting Started: First steps for organizations beginning to address AI security

Who This Is For

• Security Leaders understanding AI-specific threats
• AI/Technology Leaders assessing security requirements for AI systems
• Risk Officers evaluating AI security exposure
• Security Engineers learning AI attack vectors
• Anyone seeking an introduction to AI security threats

Why This Resource

Most security teams are experts in traditional cybersecurity but unfamiliar with AI-specific threats. This overview bridges that gap—explaining what's different about AI security in terms security professionals understand, with real incidents demonstrating that these aren't theoretical risks.

It's designed as education and awareness: help your security team understand what they're facing before diving into technical defenses.

FAQ

Q: How is AI security different from regular cybersecurity?

A: AI systems have unique attack surfaces: models can be extracted through queries, training data can be reconstructed from outputs, adversarial inputs can fool AI without triggering traditional security alerts, and supply chain attacks can introduce backdoors through pretrained models. These threats require AI-specific defenses.

Q: What are the most urgent AI security threats?

A: Prompt injection for LLMs (attackers manipulating AI through malicious inputs), model extraction (competitors stealing your AI through API queries), training data leakage (models revealing sensitive training data), and supply chain attacks (malicious models from public repositories).

Q: Is this overview enough to secure our AI systems?

A: This overview provides threat awareness and framework understanding. For comprehensive security architecture, controls, and implementation guidance, see our industry-specific premium AI Security Blueprints.