AI System Advisory & Development • AA-03

Secure AI Lifecycle Development

Build the controls into the pipeline rather than reviewing at the end.

Scope

What the engagement covers.

Retrofitting security onto a deployed model is expensive and partial. This service inserts stage gates, dataset controls and monitoring into the AI delivery lifecycle itself.

Included capabilities

Secure lifecycle requirements with stage gates from intake to retirement
Dataset, model, prompt, access, logging and change controls
AI security architecture review and reference patterns
Model registry, versioning and provenance requirements
Monitoring, incident, exception and retirement processes

Outputs and deliverables

Secure AI lifecycle standard with stage gates
Dataset, model and prompt control requirements
Security architecture review and reference patterns
Monitoring, incident and retirement procedures

Workflow

How it is delivered, step by step.

Each step has an owner, an entry condition and an artefact that has to exist before the next step begins.

01DefineLifecycle stages, gate criteria and evidence per gate.

02ControlDataset, model, prompt, access and change control requirements.

03ArchitectReference security patterns for training and inference environments.

04IntegrateGates wired into MLOps pipelines and delivery workflows.

05SustainMonitoring, incident handling, exception and retirement routines.

Use cases

Where this is typically applied.

Use case 01

Teams moving from experimentation to production deployment

Use case 02

Organizations with multiple teams building models independently

Use case 03

Environments where training data carries regulatory sensitivity

Delivery model

The operating pattern for AI System Advisory & Development.

The same delivery discipline applies across every capability in this line, so combined engagements stay coherent.

Discover

Inventory use cases, models, data, vendors, users and regulatory context.

Classify

Assess materiality by impact, autonomy, data sensitivity and deployment context.

Design

Governance, roles, policy, controls, lifecycle gates and evidence requirements.

Test

Risk assessment, architecture review, red teaming and scenario exercises.

Implement

Controls in delivery workflows, platforms, models and human oversight.

Sustain

Reporting, incidents, change, re-assessment, training and improvement.

Integration

Model registry, feature store and MLOps pipelines for lifecycle gates.
GRC platform for AI control mapping, evidence and issue management.
Data catalogue and lineage tooling for dataset provenance.
Security stack for logging, monitoring and incident handling of AI systems.

Engagement approach

Baseline engagements are a readiness and risk assessment. Build engagements operationalize the framework. Assurance engagements test models, LLM applications, agents and the governance controls around them, then validate remediation.

Other capabilities in AI System Advisory & Development.

View the full AI System Advisory & Development line →

AA-01