Service / AI System Advisory & Development

AI Secure Lifecycle Engineering

Embed security, privacy, quality and governance controls into the AI delivery lifecycle from use-case intake through retirement.

Business context

What this capability solves

Point-in-time reviews do not scale when models, prompts, datasets, retrieval sources and agent tools change continuously. A secure lifecycle turns assurance into repeatable engineering gates.

EBP Integra delivery principle

Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.

Deep-dive capabilities

Capability model

Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.

Intake Gate

Purpose, owner, risk tier, data class, autonomy and required controls.

Design Gate

Threat model, privacy/impact assessment, architecture patterns and provider review.

Build Controls

Secure coding, data controls, prompt/RAG rules, model/artifact integrity and secrets.

Evaluation Gate

Quality, safety, security, privacy, bias and tool-action tests before release.

Release Gate

Approval, version pinning, rollback, monitoring, support and evidence completeness.

Change / Retirement

Material-change triggers, re-assessment, memory/data deletion and asset retirement evidence.

Reference architecture

How the capability fits together

Final topology, control placement and deployment model are validated during discovery and detailed design.

Business & Governance

Business objectives, accountable owners, risk appetite, policy, use-case portfolio and investment priorities.

AI Engineering Lifecycle

Data, model, prompt/RAG, agent, evaluation, release, monitoring and retirement controls.

Trust & Assurance

Risk/impact assessment, security, privacy, explainability, human oversight, testing and evidence.

Enterprise Operations

Integration, observability, incident handling, change governance, model/agent lifecycle and continuous improvement.

Controls & governance

Named business and technical owner
Use-case risk classification and approval gates
Data provenance, minimization and access control
Human accountability for high-impact outcomes
Security and privacy-by-design controls
Versioned model/prompt/agent configuration
Pre-release evaluation and red-team gates
Continuous monitoring, incident and change control
Audit-ready evidence and management reporting

Priority use cases

AI SDLC transformation
MLOps/LLMOps control integration
GenAI engineering programme
Agentic AI delivery pipeline
Regulated AI release process
AI platform governance

Key deliverables

AI SDLC standard
Gate checklists
CI/CD control requirements
Evaluation suite
Release evidence pack
Change trigger matrix
Retirement runbook

Integration considerations

Enterprise IAM and workload identity
Data lake/warehouse and vector/RAG platforms
Model/API providers and private models
Application/API integration layer
MLOps/LLMOps/AgentOps and observability
SIEM/SOAR and security tooling
GRC, privacy and evidence repositories
ITSM/BPM and business workflow systems

Implementation

Phased delivery

Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.

1. AssessInventory use cases, systems, stakeholders, data, risks, maturity and constraints.

2. DesignDefine target architecture, governance, controls, delivery backlog and acceptance criteria.

3. BuildDevelop or configure AI capabilities, integrations, controls, evaluation and operating procedures.

4. AssureTest quality, safety, security, privacy, explainability and business acceptance before release.

5. OperateMonitor outcomes, drift, incidents, changes, cost, risk and control effectiveness through BAU governance.

Outcome and KPI framework

Gate complianceUnapproved release attemptsSecurity/privacy defect escape rateEvaluation regression passChange reassessment SLARetirement evidence completion