Insights
Securing and scaling AI, in plain terms.
Practical guides on deploying Claude Code, Claude for legal and teams, AI governance, and securing AI across the business and engineering.
How to Securely Deploy Claude Code Across Your Engineering Team
A practical guide to rolling out Claude Code securely: guardrails for AI-generated code, secrets protection, review gates, and governance for agentic development.
Read →Claude for Legal: Safely Adopting AI in Law Firms and Legal Teams
How law firms and in-house legal teams can adopt Claude and AI safely — protecting privilege and confidentiality while accelerating real legal work.
Read →Claude Cowork and Team Adoption: A Director's Guide to Safe Rollout
Rolling out Claude across teams (Claude cowork / Claude for teams) safely: governance, training, champions, and the controls that keep business adoption secure.
Read →Securing Enterprise AI: A Practical Governance Framework
A practical, board-ready framework for securing enterprise AI: policy, controls, identity, audit, and the process to deploy AI safely at scale.
Read →Shadow AI: How to Find It and Shut It Down Safely
Shadow AI is already in your company. Learn how to detect ungoverned AI use, the risks it creates, and how to convert it into governed, secure adoption.
Read →SOC 2 and AI: What Auditors Expect from Your AI Deployment
What SOC 2 and security auditors look for in an AI deployment — controls, logging, access, and the evidence that proves your AI is secure.
Read →Data Loss Prevention for AI Tools: Stopping Leaks Before They Happen
How data-loss prevention (DLP) for AI works: redaction, access scopes, and policy enforcement that keep sensitive data out of prompts and models.
Read →Deploying Claude Safely: A CISO's Checklist
A CISO's checklist for deploying Claude safely across business and engineering — identity, data controls, monitoring, and governance.
Read →Secure AI Coding Agents: Guardrails for AI-Generated Code
How to secure AI coding agents: scoped permissions, secrets protection, evals, and review gates that keep AI-generated code safe and fast.
Read →AI Adoption Roadmap: From Shadow AI to Governed Advantage
A staged AI adoption roadmap for directors: assess, pilot, govern, scale, and sustain — moving from shadow AI to a governed, secure advantage.
Read →Defending Against Prompt Injection in Enterprise AI
What prompt injection is, why it matters for enterprise AI, and the defenses that keep agents and assistants from being manipulated.
Read →Data Residency and AI: Keeping Sensitive Data In-Region
How to meet data-residency requirements with AI: regional controls, approved models, and policies that keep regulated data where it must stay.
Read →Red-Teaming Your AI: Why Evaluations Matter
Why AI red-teaming and evaluations are essential before production — catching unsafe behavior, regressions, and security gaps early.
Read →Golden Paths: Making Secure AI Development the Default
How golden paths make secure, fast AI development the default for every engineer — paved roads, shared tooling, and built-in guardrails.
Read →The CISO's Guide to Approving AI for the Business
How CISOs can say a confident yes to AI: the controls, evidence, and governance that make business AI adoption defensible.
Read →Writing an AI Acceptable-Use Policy That People Follow
How to write an AI acceptable-use policy that actually gets followed: clear rules, data classification, approvals, and enforcement.
Read →EU AI Act Readiness: A Practical Compliance Guide
A practical guide to EU AI Act readiness: risk classification, documentation, and the controls companies need to deploy AI compliantly.
Read →NIST AI RMF Explained for Business and Engineering Leaders
A plain-English guide to the NIST AI Risk Management Framework for directors — govern, map, measure, and manage AI risk in practice.
Read →AI for Professional Services: Adoption Without the Risk
How professional-services firms — legal, accounting, consulting — can adopt AI safely while protecting client data and confidentiality.
Read →Measuring AI Velocity: Proving Engineering ROI to Leadership
How to measure the impact of AI on engineering velocity — throughput, quality, and adoption metrics that prove ROI to leadership.
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