Getting Started with AI Security: AI Risks, How to Prevent Them, and AI for Defenders
Key Takeaways
- 1. AI expands your attack surface AI adoption is exploding across cloud environments, bringing new risks like adversarial inputs, data poisoning, model theft, and training-data exposure. Organizations need dedicated controls to keep these systems safe.
- 2. Securing AI requires a clear frameworkStrong tenant isolation, continuous audits, threat modeling, secure development, and AI-SPM tools form the core of a modern AI security strategy — and help teams manage AI risk with confidence.
- 3. AI boosts defender speed and efficiencyAI accelerates detection, automates repetitive tasks, analyzes massive datasets, and supports non-experts — helping teams bridge skills gaps and respond to threats faster.
Who is this guide for?
This guide is designed for:
Security leaders looking for a clear framework to govern AI adoption safely.
Cloud and AppSec teams who need visibility into AI services, models, and pipelines across environments.
AI/ML engineers who want to better understand security risks across model training, data, and deployment workflows.
CISOs and risk owners evaluating AI-SPM solutions or setting organizational AI policies.
What’s included
The guide covers a full, beginner-friendly overview of AI security, including:
Introduction to today’s AI landscape and why AI is appearing in 70%+ of cloud environments.
Top AI security risks — adversarial attacks, model inversion, data poisoning, model theft, and recent real-world vulnerabilities.
5 actionable defender strategies for reducing AI risk.
A buyer’s checklist and mini-RFP for evaluating AI Security Posture Management (AI-SPM) tools.
How AI empowers defenders, with people/process/technology examples for real-world security operations.
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