Thought Leadership • AI Security • Automation

Insights from the Field

Practical AI security, automation strategy, and implementation guides — written for business owners and the consultants who serve them.

All AI Security
AI Security • August 2026

Preparing for Post-Quantum Cryptography: How AI Automates Certificate Governance Across Hybrid Environments

Ask a security team how many TLS certificates their organization owns. Watch what happens. You will usually get a number, delivered with confidence, and that number will be wrong by somewhere between ...

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AI Security • August 2026

When the Attacker Is the AI Itself: What the OpenAI and Anthropic Breaches Mean for Your Security Program

In the span of nine days this July, two frontier AI labs disclosed something the security industry has been quietly dreading. Their own AI models escaped controlled testing environments and broke into...

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AI Security • July 2026

MITRE ATLAS: Architecting and Assessing AI Deployments Through Layered Defense

MITRE ATLAS catalogs 16 tactics covering the full AI attack lifecycle—from reconnaissance through impact. A kill-chain framework that maps each tactic to a defensive layer, with worked examples for a fintech lending system and a customer service chatbot...

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AI Security • July 2026

What August 2 Actually Means for Your AI Governance Program

The EU Digital Omnibus deferred Annex III high-risk obligations to December 2027. But Article 50 transparency duties and the AI Office's fining power still go live August 2 — and enterprise vendor questionnaires aren't waiting for either deadline...

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AI Security • July 2026

Managing Risk from External Dependencies: Vendor Assessment and Dependency Scanning

Every external dependency (an API, a library, a vendor platform) is a trust boundary. The moment you integrate with a third-party service or pull in an open-source package, you inherit their security ...

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AI Security • June 2026

AI Governance Is Not a New Program: It's the Gap in the One You Already Have

Every mature GRC program has the same gap right now: a control framework that wasn't designed for AI. The risk management infrastructure your organization uses — vendor assessment processes, data clas...

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AI Security • June 2026

Prompt Injection: The AI-Specific Attack Your LLM Application Cannot Ignore

Prompt injection is the SQL injection of the large language model era. But unlike SQL injection, it cannot be solved with parameterized queries or input escaping. It is a fundamental property of how l...

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AI Security • June 2026

Access Control and Authorization: Who Can Access What, and How to Enforce It

Access control is where security policy becomes operational reality. You can have perfect secrets management and bulletproof data classification, but if you don’t know who can access what—and how to e...

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AI Security • June 2026

The AI Security Policy That Enforces Itself

Every organization implementing AI — from startups to enterprises — faces the same policy gap: your existing security framework wasn’t written for a world where data flows to third-party models on eve...

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AI Security • June 2026

API Key & Token Management: Scope, Rotation, and Incident Recovery

Most teams treat API keys like passwords. That is the foundational mistake. A practical guide to scoping, rotation strategies, token types, platform-specific implementations, and incident recovery.

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AI Security • June 2026

AI Writes Insecure Code by Default: A Security-First Guide to Secrets Management

Ask any AI coding tool to write a script that calls an external API. The first draft will look like this:

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