CyberWarden

AI Security Maturity, for enterprises

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 the NIST AI RMF, then operationalised with agentic SOC, vulnerability, audit and risk workflows your team can actually run.

See the framework Book a 45-minute working session
  • 4 minransomware deployment, down from 168 hours (Fortinet 2025)
  • 24 hrstime-to-exploit new CVEs, down from 2.4 years (IBM X-Force 2025)
  • 12distinct GAI risk categories (NIST AI 600-1)
  • 95%report regulatory pressure on AI controls

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?"

Where most peers sit

Over-indexed on Protect basics and dangerously light on Govern. Knowing where you actually sit is step one.

A three-stage AI security maturity model.

  1. Protect

    Secure the AI you build and the AI you buy. Prompt-injection and jailbreak defences tested against an OWASP Agentic Top 10 red-team library, DLP on AI connectors, model and weight integrity monitoring, and secrets hygiene for agentic systems.

  2. Utilize

    Our specialty. Put agentic AI to work inside the SOC and second line: alert triage, vulnerability prioritisation, threat-hunting copilots, audit evidence and risk assistants, with humans firmly in the loop.

  3. Govern

    Make it provable. AI acceptable-use policy, an inventory of AI systems and models including shadow AI, a risk register cross-walked to your GRC framework, and board-grade reporting.

Built on NIST. Not against it.

  • NIST AI 100-1

    AI Risk Management Framework

    Core functions: Govern, Map, Measure, Manage. Trustworthy AI characteristics: valid and reliable, safe, secure and resilient, accountable and transparent, privacy-enhanced, fair and explainable.

  • NIST AI 600-1

    GenAI Profile

    Twelve GAI risk categories, including confabulation, data privacy, information security, harmful bias and value-chain integration.

  • Also mapped to

    ISO, EU, OWASP and MITRE

    ISO 42001, the EU AI Act, OWASP AI Exchange, OWASP Agentic Top 10 and MITRE ATLAS, so your work stays portable, auditable and defensible across regulators.

Map the environment. Score it. Then build it with you.

  1. 2–3 weeks

    Assess

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

  2. 1 week

    Score and prioritise

    A maturity score per stage and a risk-weighted backlog of remediations.

  3. 1 week

    Roadmap

    A 12-month plan with sequencing, ownership, dependencies and budget envelopes. You keep the board-ready maturity report.

What we have already built.

  • Agentic SOC triage

    Claude on Azure AI Foundry, wired through Model Context Protocol 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, every tool call logged, analyst-in-loop.

  • Vulnerability management

    A vulnerability process converted into detection-engineering workflows. The agent ingests Microsoft Defender, Tanium and Qualys, then orchestrates assessment, risk-weighted prioritisation, ticketing and executive reporting.

  • Implementation

    Production agentic systems, security-first by default: agentic SOC builds, compliance automation, AI security controls and hardened reference architectures. Pilot in 6–10 weeks, production hand-off in 4–6 months.

We scope per engagement, not from a rate card.

  • 4–12 weeks

    Pilot

    Advisory plus one focused build. Best fit when an exec sponsor wants a quick signal. One agentic workflow shipped end-to-end, with the maturity heatmap and 12-month plan included.

  • 3–6 months

    Program

    Full maturity uplift across all three stages. Best fit for a net-new function or major refresh: Protect controls, Utilize agents and Govern policy, with cohort training included for the team.

  • 6 months+

    Sustained

    Embedded delivery, run-state and training. Best fit when AI is core to your security strategy: roadmap, build and operate continuously, with a managed run-state available.

Scope an engagement

Tell us where your AI estate stands today.

We start with a 45-minute working session, then scope a Pilot, Program or Sustained engagement against what we find.

We have not deployed AI yet. Is this premature?

No, and it is usually the cheapest moment to act. Shadow AI is almost always already present, and setting Protect and Govern baselines before engineering ships agents avoids retrofitting controls onto production systems.

How is this different from a generic AI readiness assessment?

The Utilize stage. Most assessments stop at policy. We also build the agentic SOC, vulnerability, audit and risk workflows, and we have two of them in production with regulated customers.

Will the output survive an audit?

That is the design goal. Protect, Utilize and Govern each map onto the NIST AI RMF and the GenAI Profile, with crosswalks to ISO 42001 and the EU AI Act, so the work is reusable across regulators rather than bespoke to us.

Do your agents act on production systems?

Read-only by default. Every MCP tool call is logged and reviewable, and the agent recommends rather than auto-actions. Anything that changes state needs explicit human approval.

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