Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Text inside a pull request can act as instructions to an AI reviewer. Here is a safe, five-case test for whether your tool can be steered by what it reads.
Build agent controls when needs are narrow and stable; buy when coverage, upkeep and audit evidence outgrow a small team. Use six tests to decide.
Move an AI agent from suggesting to acting in steps, with written criteria for each promotion and a clear trigger for stepping the agent back down.
Roll out agent controls in three phases: audit to learn, warn to teach, then block what matters. Give each phase an exit test so none drags on forever.
Ask for reproduction, plain words and proof, not a ban on tools. What one major open source project asks of reporters and what your policy can borrow.
Twelve AI security terms in plain words, from agent and MCP to guardrail, gate, excessive agency and tool poisoning, so you can read a vendor page.
At 90 days, track coverage, owner assignment, scope reduction, approval quality, exceptions and time to stop. Six measures that show governance is real.
Buyers ask whether AI writes your code. Cover usage scope, review rules, dependency checks, data handling and logging without overclaiming control.
AI triage and fix suggestions change your data flow. Contract and review questions on model providers, retention, training, opt out and human approval.
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