Compliance is going to be the moat
Enterprise AI buyers will value evidence: what a system did, when, and who approved it.
ReadInsights
Category: Security
Practical security for teams without a security department: threat modelling, compliance that survives an audit, and the failures we keep seeing.
Twelve questions a board should ask before approving an AI project
Approve AI projects with written answers, measured evidence, clear ownership, data controls and a plan for failure.
ReadHIPAA and the model, what you may and may not send
HIPAA model projects need clear data categories, BAAs when PHI leaves, strong de-identification, and an audit trail showing what the model saw.
ReadWhere your data actually goes when you call a model
Most teams know their model provider. Fewer can say what sensitive text crossed the boundary, who could read it, and where copies remain.
ReadSecrets, agents, and the blast radius of a leaked token
A leaked agent token lets an attacker act across systems at machine speed, so credentials need narrow scope, short lives, real rotation, and honest…
ReadThe security questionnaire, answered in public
We publish our security questionnaire answers so clients see the real controls, gaps and limits before procurement asks.
ReadYour AI vendor is now part of your supply chain
You spent a decade learning to manage software supply chain risk. Then a model provider arrived with none of the properties that made those…
ReadPrompt injection is not a prompt problem
Prompt injection is a privilege problem, so defenses must limit what AI systems can do rather than rely on better prompts.
ReadScoped permissions are the reason businesses will actually use AI agents
AI agents need limited, temporary, revocable access before many businesses will trust them with real systems.
ReadCybersecurity Threats Every CEO Should Know
Known security failures reach the board when patching, ownership, access, backups, and routine checks are unclear or too slow.
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