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AI Governance, Security & Secure Adoption

Adopt AI without adopting its risk: governance, hardening, and adversarial testing of the systems you are putting in front of customers and staff.

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AI adoption creates real value and a genuinely new attack surface: prompt injection, jailbreaks, guardrail bypass, sensitive data exposure, unauthorized actions and agentic workflows that can be talked into doing something they were never meant to do. We validate AI systems from both sides, governance and offensive security, and connect what we find back to policy, architecture, secure development, vendor risk and remediation somebody owns.

Capabilities

What the work actually is. Scoped to the parts of it you need, at the depth the estate calls for.

AI governance framework

  • Governance structure, roles, responsibilities and approval workflow.
  • Approved, restricted and prohibited use cases, stated plainly.
  • Risk classification method for AI tools and use cases.
  • AI tool register with periodic review built in.
  • Management oversight, human review expectations and escalation paths.

AI security risk assessment

  • Data leakage, privacy, IP exposure, third-party platform risk and governance gaps.
  • Public AI platforms and enterprise deployments judged on data protection, access control and audit.
  • Handling of confidential, proprietary, customer, clinical and regulated information.
  • User access, admin privilege, authentication, sharing, integrations and logging.
  • Residual risk documented, with the controls that would close it.

AI penetration testing and adversarial assessment

  • Testing of chatbots, agents, copilots, prompt workflows and LLM-enabled applications.
  • Prompt injection, jailbreaks, system prompt extraction, instruction override and guardrail bypass.
  • Agentic workflows, tool use, action execution paths and unintended operations triggered by model output.
  • Sensitive data leakage, cross-user and cross-tenant exposure, disclosure of internal configuration or business logic.
  • Testing against the OWASP Top 10 for LLM Applications and the OWASP Web Security Testing Guide, plus manual adversarial work.
  • The surrounding application: authentication, authorization, input validation, CORS, error handling, API key handling and session behavior.

Policy, hardening and secure development

  • AI usage policy, AI security policy and employee do's and don'ts.
  • Data classification and AI data handling procedures.
  • Tool-specific hardening: access, roles, retention, logging, sharing and integration settings.
  • Post-implementation configuration review against the approved hardening standard.
  • Secure use of AI-assisted code generation, review requirements for generated code, and the training to go with it.

AI vendor and tool assessment

  • Vendor security, privacy, compliance, data retention and contractual controls.
  • Tool purpose, business owner, data categories, approved use cases and required controls.
  • Data processing, model training commitments, access control, logging and integration exposure.
  • Assessment run through Cyberwiz.ai questionnaires and evidence workflows where relevant.
  • A management summary with risks, required controls and an approval recommendation.
Business outcome

You get an evidence-based view of how your AI systems can actually be manipulated, bypassed or abused, and an operating model that says who approves a tool, what data may go into it, which use cases are permitted, and when a human has to review the output. Less unmanaged usage, clearer accountability, and an AI program that survives a customer's security review.

Deliverables

What you actually get. Scoped up front, priced fixed, and delivered by the people who scoped it.

  • 01

    AI governance framework

  • 02

    AI usage policy and AI security policy

  • 03

    Employee AI guidelines and acceptable use rules

  • 04

    Data classification and AI data handling procedure

  • 05

    AI tool register and approval workflow

  • 06

    AI security risk assessment

  • 07

    AI penetration testing report

  • 08

    Prompt injection and guardrail bypass test results

  • 09

    Agentic workflow abuse and tool-use risk assessment

  • 10

    AI vendor and tool assessment report

  • 11

    Tool-specific hardening procedures

  • 12

    Secure AI development training

How it runs

Four phases, agreed up front. The arc this practice follows, from the scoping call to the check that it held.

01
Map the AI estate
AI use cases, tools, data categories, business owners and technical scope are established first, including the usage nobody registered with anyone.
02
Review the controls
Governance documents, configurations, access, integrations, prompts and agent workflows are reviewed against how the system is actually being used.
03
Test adversarially
Then the system is attacked: prompt injection, jailbreaks, guardrail bypass, system prompt extraction, tool-use abuse and cross-tenant data exposure, against the OWASP Top 10 for LLM Applications.
04
Document and re-test
Risks, required controls, remediation priorities and governance changes are documented, then re-tested after the fixes and after any material change to model, prompts or connected tools.
Related

Rarely bought alone. The practices that most often run alongside this one.

Offensive & Defensive Security

Testing that proves what an attacker can actually reach, and the architecture work that closes it.

Cloud Security

Posture, identity and application security across AWS, Azure, GCP and SaaS, run inside the delivery workflow your engineers already use.

Compliance & Privacy

Frameworks and regulation turned into implementable controls, evidence and audit-ready documentation.

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