Sample consultancy report
Hooli: Data Security
Where AI has moved into this function, whether the controls kept up, and the one move that earns the next rung. Read in five minutes; decided in one meeting.
- Prepared on
- The aisecurityskills function diagnostic
- Basis
- Cloud Security Alliance (CSA) AI Controls Matrix (AICM) · AI Security Maturity Model (AISMM) × AI Cyber Maturity Model (AI-CMM)
- Evidence state
- Illustrative · fictional organisation
- Issued
- 18 Jul 2026
The verdict
Governed acceleration: the autonomy this function runs is autonomy its governance has earned.
Hooli's data security function reads at L3.5 governance against L2.8 autonomy: the strongest read this instrument can evidence for the function, with the gate open and positive margin. Data protection is designed in, encryption and key management run on schedule, and the most sensitive data classes carry independently verified controls. The remaining distance is not a control gap: it is the autonomy Hooli has deliberately not yet claimed while privacy-by-default coverage catches up to the standard the rest of the function already meets.
Governance
L3.5
AI Security Maturity Model (AISMM) · how well it is secured
Autonomy
L2.8
AI Cyber Maturity Model (AI-CMM) · how far AI has gone
The gate
Open
margin +0.2 · autonomy inside what governance allows
Where this function stands
The whole method in one drawing. Governance runs across, autonomy runs up, and the staircase is the gate: each governance level earned is the autonomy an organisation may responsibly claim.
What was measured
Two ladders, one instrument. Each answer maps to a Cloud Security Alliance AI Controls Matrix (AICM) control; a category claims a level only when that tier is evidenced, and an absent control caps it. The same rule scores every live run.
How well it is secured
L3.5
The CSA AI Security Maturity Model (AISMM): every answer maps to an AI Controls Matrix (AICM) control, and a level is claimed only when the tier is evidenced.
How far AI has gone
L2.8
The AI Cyber Maturity Model (AI-CMM): where the human sits in each workflow — in, on, then over the loop. our model · calibrated to SAE J3016.
Findings
Three, ranked, classified by what leadership does with each: act on a priority, protect a strength, and hold a deliberate choice.
- 01Priority
Privacy by default trails an otherwise designed-in pipeline
Protection is decided at design time across data pipelines, and impact assessments are run and refreshed for high-impact processing. Privacy defaults (minimisation, purpose limits) are applied to some personal-data flows rather than all of them: the one partial posture in an otherwise implemented category, and the natural next evidence item.
The exact control ids (for your security and governance, risk and compliance team)
DSP-07 · DSP-08 · DSP-09
- 02Strength
The most sensitive data carries verified, strongest-class controls
The highest-sensitivity data the AI touches is traced end to end with independently verified controls: the tier-4 evidence that lifts the category to its claimed level. Classification and encryption (at rest and in transit) are enforced underneath it.
The exact control ids (for your security and governance, risk and compliance team)
DSP-17 · DSP-04 · CEK-03
- 03By design
Training-data hygiene is the deliberate autonomy laggard
Four of five data workflows run assisted-to-augmented. Training-data hygiene is held a rung lower on purpose: the team keeps a human in that loop until privacy defaults reach full coverage. That restraint is what keeps the gate margin positive.
The exact control ids (for your security and governance, risk and compliance team)
DSP-01 · CEK-12
The climb
Direction, not a how-to: the next rung, and the governance that must move before autonomy does.
- Next quarter
Extend privacy-by-default (minimisation, purpose limits) from some personal-data flows to all of them, closing the single partial posture in the lead category.
- Two quarters
With privacy defaults at full coverage, lift training-data hygiene one autonomy rung and re-assess: the gate stays open only if governance moves first.
- Continuous
Hold the verified-evidence bar on the most sensitive data classes through each key rotation and pipeline change; re-run the diagnostic after material changes.
About this instrument
What a reader should carry out of the room: how the diagnostic works, how progress is tracked, and what the practice is for.
One questionnaire, two reads
Every answer maps to a Cloud Security Alliance AI Controls Matrix (AICM) control. Read one way, the answers grade the function: governance versus autonomy, joined by the gate. Read the other way, the same answers name the skills each person in the function must acquire. Diagnosis and reskilling from one sitting.
Tracked, not judged
The first run is a baseline, never a verdict. Re-assess after the work and the radar overlays the previous run, so leadership sees movement, not a grade. The compatible-standard packs (ISO/IEC, the National Institute of Standards and Technology, and the CSA AI Consensus Assessments Initiative Questionnaire) are lenses on the same answers: assess once, report many ways.
Direction, not a solution
AI is a moving target, so the report names the next rung and the governance that must move first — never a vendor stack or a how-to. The gate keeps the climb honest: autonomy is claimed only after the controls that catch it are in place.