What's new · Compliance
Answer a control once. Every framework that shares it gets credit.
Compliance programmes waste enormous time re-answering the same question for every framework that happens to ask it slightly differently — MFA enforcement shows up in Cyber Essentials, ISO 27001, GDPR, and half a dozen others, worded differently each time but asking the same thing underneath. Re-gathering the same evidence five separate times isn't rigour. It's duplication.
How the reuse works
786 Cyber maps every framework's controls down to a smaller set of canonical controls — a normalised control taxonomy shared across frameworks — so answering a control once, in one framework's wizard, automatically credits every other framework that maps to the same canonical control.
The Compliance Vault shows this working in practice: per-framework progress rings, section-by-section breakdowns, and an AI-generated gap analysis that tells you what's genuinely missing versus what's just not yet mapped.
What it maps to
Every framework 786 Cyber supports — this is the connective tissue across all of them, not a single-framework feature. It's also the same underlying mechanism behind Capability Gap Analysis: evidence reuse is what makes "capabilities, not a thousand controls" possible in the first place.
Frequently asked questions
What is a canonical control?
A normalised control shared across frameworks. Many frameworks ask the same underlying question in different words — MFA enforcement appears in Cyber Essentials, ISO 27001, GDPR and others. 786 Cyber maps each framework control down to a shared canonical control, so answering it once can credit every framework that maps to it.
Does answering one control automatically mark the others as met?
Cross-mapped equivalents are surfaced for your review — they are never silently marked as met. The mapping does the work of finding them; you stay in control of confirming them.
Where do I see this working?
The Compliance Vault shows per-framework progress rings, section-by-section breakdowns, and an AI-generated gap analysis that separates what is genuinely missing from what is simply not yet mapped.
Prove it once, everywhere it counts
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Where to go next
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