Governance, risk, and compliance teams write policies, maintain spreadsheets, collect screenshots, and answer questionnaires. This pillar asks how much of that evidence the systems themselves can produce.
The conversion
Much of the work in GRC turns system data into reports on a schedule. Machines can do more of that without weakening oversight.
People still have to decide which risks to accept, which exceptions are reasonable, what the evidence means, and when to stop an activity. Report preparation often leaves too little time for those decisions.
Machines can run policy checks, collect evidence, spot changes, track fixes, and keep records ready for an audit.
Where the engineering sits
Software engineering already has useful ways to handle each layer. Security and compliance teams can use them too.
| Governance artifact | Engineering equivalent | What it produces |
|---|---|---|
Written policy |
Machine-readable policy with an enforced rule |
A statement that can be tested, versioned, and diffed |
Control narrative |
Controls as code with an executable test |
A pass, fail, or unknown result on a schedule |
Evidence screenshot |
Evidence pipeline with query provenance |
An artifact an auditor can reproduce |
Annual control review |
Continuous test with drift alerting |
Time between drift and detection as a metric |
Risk register |
Linked findings with measured blast radius |
Prioritisation based on reach rather than severity labels |
Audit preparation |
Standing evidence store |
An audit that reads rather than assembles |
Related work at the firm
This pillar connects to existing research on AI-native software, observability, and evidence-bearing operations. Work on Operational Truth supplies the state distinctions used throughout, and work on unbundling knowledge work supplies the method for deciding which governance tasks should be human, machine, shared, redesigned, or removed.
These links provide research context. They do not claim that a product solves the problem.
What would prove us wrong?
The pillar assumes automated evidence production improves governance rather than merely accelerating it.
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Evidence that automated evidence volume reduces the attention paid to each item, producing worse decisions than a smaller manual set.
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Auditor behaviour showing that machine-collected evidence is discounted or re-requested in human form, adding cost rather than removing it.
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Cases where the effort to encode controls exceeded the recurring effort it replaced, for environments below a certain size.