Sample consultancy report
Globex: Application & DevSecAIOps 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
Governance ahead of adoption: AppSec policy and testing exist, but agent boundaries and protection-by-design still cap the climb while most build workflows stay human-run.
Globex's application and DevSecAIOps security function reads at L2.2 governance against L1.2 autonomy: an industrial posture where the gate is open because adoption has not outrun the evidence. Identity and access foundations reach Defined; AppSec policy and testing are in place. What holds the composite down is the absent agent-boundary control, absent protection-by-design on AI data pipelines, and named supervision that is only partial. Code review is assisted; dependency scanning, threat modeling, AppSec testing, and model-build assurance remain largely manual — governance must close those gaps before autonomy claims another rung.
Governance
L2.2
AI Security Maturity Model (AISMM) · how well it is secured
Autonomy
L1.2
AI Cyber Maturity Model (AI-CMM) · how far AI has gone
The gate
Open
margin +0.8 · 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
L2.2
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
L1.2
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.
Held below the next tier: tier 4 not yet evidenced
Held below the next tier: tier 3 not yet evidenced
Held below the next tier: DSP-07 absent
Held below the next tier: AIS-11 absent
Held below the next tier: tier 3 not yet evidenced
Findings
Three, ranked, classified by what leadership does with each: act on a priority, protect a strength, and hold a deliberate choice.
- 01Priority
Agent boundaries and protection-by-design are still absent
App Security caps at Repeatable because agent tool, data, and action boundaries are not defined, and Data Security caps because protection-by-design is absent on AI pipelines. Those two absents are the board priority: without them, Defined evidence in AppSec and data cannot be claimed even though policy and testing already run.
The exact control ids (for your security and governance, risk and compliance team)
AIS-11 · DSP-07 · AIS-05
- 02Strength
Identity foundations reach Defined while AppSec policy is written
An identity and access policy covers AI systems, least privilege is enforced on models and pipelines, and an application security policy plus testing cover AI-enabled apps. Classification of AI data flows is implemented underneath. That floor is what keeps governance ahead of the still-manual adopt average.
The exact control ids (for your security and governance, risk and compliance team)
IAM-01 · IAM-05 · AIS-01 · DSP-04
- 03By design
Most build workflows stay human while governance finishes Defined
Code review runs assisted; dependency scanning, threat modeling, AppSec testing, and model-build assurance stay manual on purpose. Engineers are trained on acceptable use, but named human supervision of AI-assisted engineering is only partial. That restraint is what keeps the gate open with positive margin.
The exact control ids (for your security and governance, risk and compliance team)
HRS-15 · GRC-15 · AIS-03
The climb
Direction, not a how-to: the next rung, and the governance that must move before autonomy does.
- Next quarter
Define agent security boundaries (tools, data reach, allowed actions) and apply protection-by-design to AI data pipelines so AppSec and Data Security can evidence Defined.
- Two quarters
Name accountable supervision for AI-assisted engineering work and close the access-review and AppSec-metrics partials so Capable evidence matches the autonomy the team is ready to claim on code review.
- Continuous
Hold AppSec testing and least-privilege enforcement through each model and pipeline change; re-run the diagnostic before lifting dependency or model-build workflows another autonomy rung.
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.