cyberwarden · maturity model

AI Security: From Risk to Readiness.

A practical maturity-led path to deploying — and defending — AI in the enterprise. Honest assessment across Protect, Utilize, and Govern, mapped to NIST AI RMF, then operationalized with agentic SOC, vulnerability, audit, and risk workflows your team can actually run.

See the framework → Book a 45-min working session
01 layer · why now
Why now

AI is moving faster than your security stack.

Machine-speed threats. Human-speed defenses. The gap is widening — and adoption pressure isn't waiting for your governance to catch up.

4 min
Ransomware deployment — down from 168 hrs (Fortinet 2025)
1 day
Time-to-exploit new CVEs — down from 2.4 yrs (IBM X-Force 2025)
12
Distinct GAI risk categories (NIST AI 600-1)
95%
Report regulatory pressure on AI controls
Security leader weighing AI adoption pressure against protection requirements
The security leader's dilemma

Adoption without a security frame is risk theatre. Refusal isn't an option either.

Pressure to adopt

  • Business demands productivity gains from AI
  • Engineering is shipping agents — with or without sign-off
  • Competitors publicising AI-driven outcomes
  • Board expects an AI strategy — yesterday

Pressure to protect

  • Data leakage through prompts and connectors
  • Hallucinated outputs reaching customers or auditors
  • Shadow AI tools bypassing every control you own
  • No baseline for "how secure is secure enough?"
02 layer · framework
Our framework

A three-stage AI security maturity model.

Every organisation sits somewhere on this curve. Knowing where is step one.

01

Protect

Secure the AI you build and the AI you buy. Defend against attacks on the model itself.

→
02

Utilize

Safely operationalise AI inside the security function: agentic SOC, vulnerability, audit, and risk.

Our specialty
→
03

Govern

Policy, accountability, measurement. Prove to the board, regulators, and customers it's under control.

Where most of your peers sit → Where best-in-class are heading

Securing the AI estate Stage 01 · Protect

Protect — Secure the AI itself.

Defend the models, agents, prompts, training data, and connectors that make up your AI estate — against tampering, exfiltration, poisoning, and misuse.

Prompt-injection & jailbreak defenses

Tested against an evolving red-team library mapped to OWASP Agentic Top 10.

Data loss controls on AI connectors

DLP scoped to where models actually pull from — RAG, MCP, agents.

Model & weight integrity monitoring

Detect tampering across the supply chain.

Secrets hygiene for agentic systems

Short-lived creds, scoped tool permissions, auditable tool calls.

Inference & tool data governance

Consistent org-wide controls on what AI can read, write, and call.

Agentic AI inside the SOC Stage 02 · Utilize Our specialty

Utilize — Turn AI into a force multiplier.

Put agentic AI to work inside the SOC and the second line — triage, investigation, evidence collection, correlation, control testing — with humans firmly in the loop.

Agentic alert triage

Cut analyst toil on repetitive L1 patterns. Read-only by default, auditable tool calls.

Vulnerability management & prioritisation

Risk-rank CVEs against exposure, exploitation, and business impact.

Threat hunting copilots

Hypothesis-driven hunts over XDR / SIEM data.

SecOps workflows orchestrated by agents

Multi-step playbooks executed under analyst oversight.

Audit & compliance evidence

Pull, normalise, and map control evidence automatically — continuously.

Risk & policy assistants

Draft, redline, and check against governance baselines.

Making AI provable to the board and regulators Stage 03 · Govern

Govern — Make it provable.

Policy, accountability, and measurement — so AI risk can be reported on the same page as every other risk in the enterprise.

AI use & acceptable-use policy

Aligned with NIST AI RMF Govern function.

Inventory of AI systems & models

Tied to data classifications and owners — including shadow AI.

Risk register & control mapping

Cross-walked to your existing GRC framework.

Board-grade reporting

Metrics leadership can actually act on.

03 layer · alignment
Built on NIST. Not against it.

Portable, auditable, defensible — by construction.

Our Protect / Utilize / Govern stages each map cleanly onto the NIST AI Risk Management Framework and the Generative AI Profile, so your work is reusable across regulators and frameworks.

// NIST AI 100-1 · RMF

Core functions: Govern · Map · Measure · Manage.

Trustworthy AI characteristics: Valid & Reliable · Safe · Secure & Resilient · Accountable & Transparent · Privacy-Enhanced · Fair · Explainable.

// NIST AI 600-1 · GenAI Profile

12 GAI risk categories, including:

Confabulation · Data Privacy · Information Security · Harmful Bias · Value Chain & Component Integration · and more.

Also mapped to ISO 42001 EU AI Act OWASP AI Exchange OWASP Agentic Top 10 MITRE ATLAS
04 layer · the gap
The gap

Where most enterprises sit today.

Self-rated maturity from a sample of mid-market and enterprise security functions. Most are over-indexed on "Protect basics" and dangerously light on "Govern."

Protect
Utilize
Govern
Optimised
8%
3%
2%
Defined
22%
12%
9%
Repeatable
44%
31%
24%
Ad-hoc
26%
54%
65%

Higher % = more orgs concentrated at that level. Sources: Gartner AI TRiSM Hype Cycle 2025; ISACA State of Digital Trust 2025.

05 layer · services
Our services

Four ways we help.

Pick the engagement that fits — training, advisory, or build. Each one ladders back to the maturity model.

01 · TIER

Individual Training

For analysts, engineers, auditors who need to skill up — fast. CCA-F exam sponsored.

See training →
02 · TIER

Corporate Training

Cohort-based programs tailored to SecOps, audit, risk and compliance.

See cohort programs →
03 · TIER

Advisory

Environment review → maturity scoring → prioritised 12-month roadmap.

See advisory →
04 · TIER

Implementation

We build and deploy the agentic systems alongside your team.

See implementation →
Tier 03 · Advisory

Map the environment. Score it. Hand you a plan you can execute.

1

Assess

2–3 weeks

Environment review, stakeholder interviews, AI inventory, current-state heatmap.

2

Score & prioritise

1 week

Maturity score per stage. Risk-weighted backlog of remediations.

3

Roadmap

1 week

12-month plan with sequencing, ownership, dependencies, and budget envelopes.

Deliverable: A board-ready maturity report + 12-month execution plan — yours to keep.
Tier 04 · Implementation

When the plan is set, we build it with you.

Production agentic systems, security-first by default.

SOC

Agentic SOC builds

Triage, enrichment, vulnerability assessment, and hunting agents wired to your XDR / SIEM / case management.

CMP

Compliance automation

Evidence-collection agents mapped to your control framework — SOC 2, ISO 27001, PIPEDA, NIST.

SEC

AI security controls

DLP for AI connectors, prompt-injection defenses, model integrity checks, runtime guardrails.

ARC

Hardened reference architectures

Bedrock / Azure / cloud-native — pick your stack, we secure it.

Pilot in 6–10 weeks · production hand-off in 4–6 months.

06 layer · proof points
Proof points

What we've already built.

Two reference builds you can poke at — both in production with regulated customers.

Agentic SOC triage Reference Build 01

Agentic SOC Triage MVP

Claude on Azure AI Foundry, wired through Model Context Protocol (MCP) to Cortex XSIAM, Microsoft Defender XDR, and Wiz. The agent triages alerts, drafts the analyst narrative, and hands off — with a clean audit trail.

  • Read-only by default — investigate without changing production state
  • Auditable tool calls — every MCP action logged & reviewable
  • Analyst-in-loop — recommends; never auto-actions without approval
Agentic vulnerability management Reference Build 02

Vulnerability Management MVP

Vulnerability process converted into detection-engineering workflows. Agent ingests data from Microsoft Defender, Tanium, and Qualys — then orchestrates assessment, prioritisation, ticketing, and reporting.

  • Multi-tool ingest — Defender, Tanium, Qualys normalised in one view
  • Risk-weighted prioritisation — exposure × exploitability × business impact
  • Ticket & report automation — ITSM tickets and exec reports out the other side
07 layer · engagement
Engagement model

Five clear phases. You can step off at any handover.

No 200-page deliverables nobody reads. Outcomes, deployed.

1

Assess

Wk 1–2

Discovery, interviews, AI inventory.

2

Score

Wk 3

Maturity heatmap + prioritised backlog.

3

Roadmap

Wk 4

12-month sequencing with budget envelopes.

4

Build

Wk 4+

Optional: reference implementation + handover.

5

Operate

Post-build

Managed run-state or coaching once build is live.

Engagement sizing

We scope per engagement — not from a rate card.

Every environment is different. Pick the shape that fits today; you can resize at any handover.

Shape · Pilot

4–12 weeks

Advisory + one focused build

  • Best fit: first proof — exec sponsor wants quick signal
  • One agentic workflow shipped end-to-end
  • Maturity heatmap + 12-month plan included
Scope a pilot
Most common
Shape · Program

3–6 months

Full maturity uplift across stages

  • Best fit: net-new function or major refresh
  • Protect controls + Utilize agents + Govern policy
  • Cohort training included for the team
Scope a program
Shape · Sustained

6 months+

Embedded delivery + run-state + training

  • Best fit: AI is core to your security strategy
  • Roadmap → build → operate, continuous
  • Managed run-state available
Scope sustained engagement
Why us

Build + train + run — same team.

The same practitioners that design your roadmap can deliver it.

// Lead · Security & AI Engineering

CyberWarden — cybersecurity consulting with deep financial and energy-sector experience. Built and operates the agentic SOC and VM reference architectures. Practitioner-led across Claude, AWS Bedrock, Azure Copilot & AI Foundry — in production.

// Co-delivery partners

Curriculum and delivery co-developed with partners running production AI in regulated environments — energy, finance, healthcare. Sector strengths matched to your industry. References on request.

Let's talk

Ready to put a number on your AI security maturity?

A 45-minute working session. Walk us through your environment. Leave with a tentative maturity score, three concrete next steps, and a written proposal within five business days.

  • SANS AISMM-aligned assessment + NIST AI RMF mapping
  • Agentic SOC, VM, audit, and risk workflows in production
  • Pilot / Program / Sustained shapes — we scope to fit
  • Practitioner-led, not slideware
Services you're interested in (optional · Maturity Assessment pre-selected)

We respond within one business day.