← Intelligence Hub Executive Briefing · 27 May 2026
Severity: Elevated 10-min briefing
An Executive Briefing

AI is already
in your enterprise.
Your security program
may not be.

The board is asking what security is doing about AI. The business has already shipped Copilot, signed enterprise ChatGPT, and approved agent pilots, often before security saw the procurement request.

AISMM / A Strategic Briefing for CISOs / MSc in Cybersecurity · CISSP
Why now

AI didn't wait
for security.

T-12 months
Procurement signed the enterprise ChatGPT contract.
T-8 months
Copilot rolled out to every employee in M365.
T-3 months
Marketing stood up an agent platform over the weekend.
T-1 month
App teams wired LLMs into customer-facing products.
Today
Security is asked what the program is doing about AI.

Traditional playbooks don't fully translate. Shadow AI, agentic systems, and MCP tools expand the attack surface in ways most programs aren't yet instrumented to see.

Scope, precisely defined

A maturity model for the
security program, not
the AI project.

What AISMM Is
✓A structural map of capabilities a program needs to safely adopt AI
✓A roadmap for conscious, defensible investment decisions
✓Aligned with the CSA AI Controls Matrix and AI CAIQ
✓Built on the same backbone as the CSMM
What AISMM Is Not
✕A checklist for a single AI application
✕A measure of how good you are at building AI
✕A mandate to reach Level 5 everywhere
✕A model for how security teams themselves use AI
It answers one question. What does our security program need to look like to safely adopt and secure AI across the enterprise?
The model at a glance

Three domains.
Twelve categories.
Five maturity levels.

Foundational The baseline
Governance
Org Management
IAM
Security Monitoring
Structural Securing the build
Infrastructure
Model Security
App Security
Data Security
Procedural Sustaining over time
Risk & Provider
AI Dev & Supply Chain
Privacy & Compliance
Incident Response

Twelve categories. One operating picture. Where the AI Council, security architecture, and the business meet.

The journey, not the destination

No one targets
Level 5 everywhere.

1
Initial
No coordinated AI security. Teams self-manage with self-selected tools.
2
Repeatable
Initial usage policies. Visibility via billing, telemetry, surveys.
3
Defined
AI Council, deployment registry, standardized baselines, AICM adoption.
4
Capable
Automation, policy-as-code, deterministic guardrails, attested conformance.
5
Efficient
Continuous, self-improving, intent-binding, threat-intel-driven response.
→ Realistic enterprise target zone for most categories ←

Target Level 3 to 4 across foundational and structural categories, with selected Level 4 to 5 capabilities where risk warrants it. Defending "we chose not to" is a sign of maturity, not weakness.

From repeatable to capable

The capabilities that
actually discriminate.

01
AI Council
Real decision rights, cross-functional, approval authority.
02
Approved Registries
Use cases, models, providers with pinned versions.
03
AI-SPM Discovery
Continuous AI posture via AI-SPM, CSPM, CASB.
04
SIEM Telemetry
Prompts, responses, agent actions, guardrail violations.
05
Per-Agent NHIs
Non-human identities federated to enterprise IdP.
06
Runtime Guardrails
Bedrock, Azure AI Content Safety, OpenAI moderation.
07
AI-BOM
Bill of materials with model provenance verification.
08
AI Incident Response
Playbooks for prompt injection, agent hijacking, MCP abuse.

Not aspirational. This is what separates a defined program from a capable one.

The risk landscape

The exposures the
model is built to address.

APP
Prompt injection
App layer · agents · MCP
APP
Agent hijacking
Unconstrained tool execution
IAM
MCP misuse
Unscoped authorization
DAT
RAG data leakage
Vector stores · embeddings
DAT
Insecure retrieval
RAG ignoring source ACLs
DAT
Training data poisoning
Supply chain · ingest
ORG
Unmanaged providers
Shadow AI services
IAM
Weak agent identity
Static keys · broad scopes
MON
Missing telemetry
No prompts · responses
DEV
AI supply-chain exposure
Models · libs · assistants
The data side of AI is where most organizations are weakest today.
Why governance is the foundation

If governance and identity fail,
the rest of the program is aspirational.

What mature governance looks like
AI Council with real authority
Decision rights, not advisory.
Authoritative registry
Use cases, models, providers.
Policy-as-code
Enforced in CI/CD, not in PDFs.
Ethics review for high-risk
Cross-functional, documented.
Role-based AI training
Measurable completion, tied to access.

Security cannot govern enterprise AI alone. It is cross-functional by design.

For your next executive review

Six things to take
to the board.

1
Maturity follows the cloud curve.Compressed timeline, less forgiving of delay.
2
Reactive governance fails at scale.Get ahead of procurement, not behind it.
3
Visibility precedes governance.You cannot govern what AI-SPM hasn't discovered.
4
Identity is the perimeter, again.Agents and NHIs concentrate the weak points.
5
Telemetry is the prerequisite.No prompts in SIEM means no AI risk program.
6
Target Level 3 to 4 deliberately."We chose not to" is a sign of maturity.

Pick one. Run it through your next executive review.

Most enterprises already have AI risk.
Few have AI maturity.

The AISMM gives security leaders the structure to make deliberate, defensible decisions about how mature each part of the program should be, and to defend those decisions to the board, to auditors, and to themselves.

How is your organization measuring AI security maturity today?
#AISecurity #CyberSecurity #AIGovernance #GenAI #RiskManagement #CISO #AISMM
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