# For risk and compliance: know where you stand.

> AI risk and compliance management, in ColossalX, is a register that fills itself from compliance gaps, audits, scans, proven attacks and intelligence, AI risk quantified in money with FAIR, frameworks assessed from live signals with one score per framework, and an audit lifecycle that runs from a risk-based plan to a sealed, timestamped archive.

A risk register that fills itself, AI risk in money, controls assessed from live signals and audits that end in a sealed archive.

Canonical page: https://colossalx.tech/solutions/risk-compliance · Last reviewed: 6 Oct 2026

*Illustration:* Audit archive · sealed: Plan Risk-based, approved; Tests Design, then operation; Samples Replay identically; Findings With management response. Sealed and timestamped.

## The question you are asked

Where do we stand against our frameworks, in money and in evidence?

## What auditors ask, and what answers it.

- **The threat:** The risk register is a spreadsheet, current for one committee meeting. **The control:** The register fills itself from gaps, audits, scans, proven attacks and intel, quantified with FAIR. [Risk quantification](https://colossalx.tech/platform/risk-quantification)
- **The threat:** A control shows green because nobody could test it. **The control:** A control nobody could assess is marked not assessable and excluded, never quietly passed. [Compliance and AI governance](https://colossalx.tech/platform/compliance)
- **The threat:** The same person prepared and reviewed the audit work. **The control:** The preparer never reviews and the signer never prepares; the archive is sealed. [Audit](https://colossalx.tech/platform/audit)

*Screen, from a demo workspace:* A FAIR analysis in a demo workspace: one AI-governance gap expressed as an expected annual loss in money, with the range it is likely to fall in and the expected loss in the tail beyond a one-in-twenty-year event. Callouts: 1. Expected loss per year 2. The likely range 3. The tail beyond it

## Questions the audit committee will ask.

- **Where do we stand against our frameworks?** One score per framework, trended nightly, with not assessable shown as an answer.
- **What is our AI risk in money?** A FAIR loss range with its tail, held against the board appetite.
- **Will the audit trail hold up?** Preparer never reviewer, signer never preparer, and a sealed, timestamped archive.

*Illustration:* One control · three frameworks: Guardrails on AI inputs and outputs maps to EU AI Act (Art. 15); NIST AI RMF (MANAGE-2.1); ISO/IEC 42001 (Clause 8.3). Mapped to and assessed against, not certified.

## Brief: Risk and compliance, mapped and evidenced

ColossalX, from Quantexra Labs, is an AI security and governance platform delivered as SaaS. It keeps a quantified AI risk register, assesses controls from live signals and runs audits.

- A self-filling register, quantified with FAIR.
- Controls assessed from live signals, scored per framework.
- Audits from risk-based plan to sealed archive.

**Ask any vendor, including us**

- What does an untested control show?
- Can a finding close without positive evidence?
- Who prepared this work, and who reviewed it?

**Limit:** Jurisdiction packs are starting content for counsel; no certification is held.

## What ColossalX does not do

- Jurisdiction-pack content and regulator page-watch candidates are starting points for your counsel to review.
- Cloud posture is assessed for AWS only.
- ColossalX holds no certification; frameworks are mapped to and assessed against.

*Illustration:* Control validation · one control: Status Not assessable; Reason No source could be read; Score Excluded, not failed; Decided by A named person. Reason recorded.

## Questions

### How do you manage AI model risk?

Start with an inventory of the models, agents and third-party AI in use, assess each one, then test, monitor and record who decided what. ColossalX keeps that inventory, flags AI vendors that are in use but not assessed, quantifies risk in money with FAIR and holds it against your risk appetite, escalating acceptances beyond appetite to the board.

### How are AI controls tested and evidenced?

Controls are assessed from live signals, and recent tests count as control evidence. Each control carries a health label beside the status a person decided, with a reason per control. Evidence collects itself, graded A to D by how it was obtained, verified by a second person, timestamped daily and withdrawn, never deleted.

### How does ColossalX track regulatory change?

Jurisdiction packs for global, India, EU and US rules carry regulatory reporting clocks, and a regulator page-watch raises candidates that a person confirms. The content is a starting point for your counsel to review, not legal advice, and a compliance calendar is built from your own records.

### Can risks and findings sync to our GRC tool?

Yes. Risks sync out to GRC, ticketing and third-party risk tools, and findings become issues with an owner, a due date and a ticket in the tool that will close it. An issue closes only on positive evidence, not on a ticket status, and a risk acceptance must expire.

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ColossalX is an AI security and governance platform from Quantexra Labs LLP, delivered as SaaS. Book a walkthrough: https://colossalx.tech/demo · client.success@quantexra.tech