AI SECURITY OPERATIONALIZATION LOOP

From business need to adequately controlled AI deployment

Six connected stages that structure the process of securing AI use from a stated business purpose to owned, testable and continuously monitored controls.

Select the option that fits best.

Input

  1. A business request to introduce AI into a workflow, product, service, or decision.
  2. or, as an alternative, An unregistered AI tool identified in use.
  3. or, as an alternative, A material feature or model change to an AI tool already in use.
  4. or, as an alternative, An incident caused by AI use.
  • Template: AI use case formTBD
  • List: AI discovery toolsTBD
  • Template: AI change request formTBD
  • List: AI configuration management & regression testing toolsTBD
  • Template: AI incident reportTBD

Main activities

  1. Record the business workflow, proposed AI use, AI tool, or AI incident that triggered the stage.
  2. Identify the accountable business owner and the product or technical owner.
  3. Record the affected users, systems, data, connected tools, decisions, actions, and expected degree of autonomy.
  4. State the intended business benefit and the worst credible adverse business impact.
  5. Decide whether the use case or tool should proceed to a bounded experiment and identify any immediate constraints.
  • Process: New AI use-caseTBD
  • Process: Shadow AI identificationTBD

Output and evidence

  1. A concise AI use-case or tool record.
  2. A named accountable business owner.
  3. A named product or technical owner.
  4. An initial view of affected architecture, data flows, identities, permissions, and connected tools.
  5. An initial statement of autonomy and worst credible adverse business impact.
  6. A decision to proceed, revise, pause, or stop.
  • Template: New AI use case specificationTBD

Ownership and responsibilities

  1. Business owner

    Defines the business purpose, expected value, acceptable use, and adverse business impact.

  2. Product or technical owner

    Records how the AI capability is implemented, connected, accessed, and changed.

  3. Security, legal, compliance and privacy

    Identify immediate information gaps, prohibited uses, and conditions that must constrain the next stage.

Common blockers

  1. The request is described only as a product name rather than a business activity or workflow.
  2. The AI tool or capability is discovered only after broad use has begun.
  3. No accountable business owner is willing to own the purpose and resulting risk.
  4. A material feature or model change is treated as routine software maintenance even though it may alter behavior or exposure.

Known gaps and open questions

  1. Model and feature change management:

    Determine which AI changes should be governed directly through this Playbook and which should be handled by specialist assurance tools. Current hypothesis: this Playbook should capture the resulting risk and required control capability, while automated regression testing of affected workflows against evaluations should sit within specialist assurance tooling as required based on the defined risk appetite.

  2. Where should AI red-teaming live?

Last reviewed Current focus: Document initial thesis, gather research evidence and implementation examples.
  1. Input

    1. A business request to introduce AI into a workflow, product, service, or decision.
    2. or, as an alternative, An unregistered AI tool identified in use.
    3. or, as an alternative, A material feature or model change to an AI tool already in use.
    4. or, as an alternative, An incident caused by AI use.
    • Template: AI use case formTBD
    • List: AI discovery toolsTBD
    • Template: AI change request formTBD
    • List: AI configuration management & regression testing toolsTBD
    • Template: AI incident reportTBD

    Main activities

    1. Record the business workflow, proposed AI use, AI tool, or AI incident that triggered the stage.
    2. Identify the accountable business owner and the product or technical owner.
    3. Record the affected users, systems, data, connected tools, decisions, actions, and expected degree of autonomy.
    4. State the intended business benefit and the worst credible adverse business impact.
    5. Decide whether the use case or tool should proceed to a bounded experiment and identify any immediate constraints.
    • Process: New AI use-caseTBD
    • Process: Shadow AI identificationTBD

    Output and evidence

    1. A concise AI use-case or tool record.
    2. A named accountable business owner.
    3. A named product or technical owner.
    4. An initial view of affected architecture, data flows, identities, permissions, and connected tools.
    5. An initial statement of autonomy and worst credible adverse business impact.
    6. A decision to proceed, revise, pause, or stop.
    • Template: New AI use case specificationTBD

    Ownership and responsibilities

    1. Business owner

      Defines the business purpose, expected value, acceptable use, and adverse business impact.

    2. Product or technical owner

      Records how the AI capability is implemented, connected, accessed, and changed.

    3. Security, legal, compliance and privacy

      Identify immediate information gaps, prohibited uses, and conditions that must constrain the next stage.

    Common blockers

    1. The request is described only as a product name rather than a business activity or workflow.
    2. The AI tool or capability is discovered only after broad use has begun.
    3. No accountable business owner is willing to own the purpose and resulting risk.
    4. A material feature or model change is treated as routine software maintenance even though it may alter behavior or exposure.

    Known gaps and open questions

    1. Model and feature change management:

      Determine which AI changes should be governed directly through this Playbook and which should be handled by specialist assurance tools. Current hypothesis: this Playbook should capture the resulting risk and required control capability, while automated regression testing of affected workflows against evaluations should sit within specialist assurance tooling as required based on the defined risk appetite.

    2. Where should AI red-teaming live?

    Last reviewed Current focus: Document initial thesis, gather research evidence and implementation examples.

PUBLICATION LOG

Recently added or revised

A record of material changes to the public playbook.

Public playbook change record
DateStageChangeStatus
Playbook foundation Initial Draft created and the six-stage playbook structure established. Draft