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Module DAT-05 sigil: Data pillar, Strategy layer, maturity bands 1 to 3.Deterministic sigil for Module DAT-05. The Pillar geometry encodes Data (Pillar 2); the top-right marker S encodes the Strategy layer; the baseline meter encodes maturity bands 1 to 3.SDAT-05

P2

L-E

DAT-05

Integration Architecture Blueprint

Per-engagement blueprint for integrating legal department systems with AI tools through a three-tier architecture with canonical Agentic Tier API provisions, Class 6 Shadow AI detection controls, and Risk Taxonomy 2026 cross-walk.

ModuleAdvancedPer-engagementAdoption lensDefensibility lens

Audience

GC / CLOLegal Operations

·

6–12 months for initial rollout; 1–2 weeks for annual review and major change assessments.

Executive Summary

DAT-05 defines the Integration Architecture Blueprint for connecting AI capabilities to core legal technology in a secure, scalable and defensible way. It provides a three-tier reference architecture, security and compliance controls, and an implementation roadmap that align with the AI Bill of Materials (AI BoM), data governance, and the AI Task Force charter. The module is used during initial AI platform design, major system upgrades, and annual reviews to ensure that document management, CRM, eBilling, matter management, and communication systems integrate with AI tools through an API-first, event-driven, vendor-neutral architecture. It embeds Class 6 Shadow AI detection, Agentic Tier (Level 4) safeguards, and Risk Taxonomy 2026 controls into the integration layer. Outputs include integration design artefacts, testing and validation frameworks, governance processes, and DPS-grade evidence for regulators and clients. When executed, DAT-05 reduces breach and privilege risk, supports GDPR, EU AI Act and ABA compliance, and enables measurable ROAI across Protect, Comply, Grow and Transform quadrants.

Metric 0 Pre-Check

Complete all five gates before any integration architecture design or implementation:

  • Gate M0.1 — GOV-02 verified: AI Use Policy is current; AI usage categories define permissible integration patterns.
  • Gate M0.2 — AI BoM verified: All AI systems to be integrated are registered in the AI Bill of Materials.
  • Gate M0.3 — DAT-01 verified: Data Governance Framework is in force; integration must respect data rules per category.
  • Gate M0.4 — STR-07 verified: AI Task Force Charter is active; escalation channels open for Class 6 Shadow AI.
  • Gate M0.5 — VEN-04 verified: AI Vendor Security Checklist completed for all AI vendors in scope.

All five gates must be confirmed before integration work begins.

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1. Executive Summary and Strategic Context

Integration vision: Enable secure, compliant integration between core legal systems and AI capabilities, creating a unified ecosystem that enhances legal service delivery while preserving confidentiality, security, and professional responsibility.

Business objectives include: improving operational efficiency, maintaining security and compliance, enabling scalable AI, reducing complexity, supporting resilience, and demonstrating ROAI across Protect, Comply, Grow, and Transform.

Architectural principles:

  1. Security by design
  2. API-first approach
  3. Event-driven architecture
  4. Data minimisation (DAT-04)
  5. Vendor neutrality
  6. Defensible AI governance (AI BoM, Agentic Tier, Class 6 detection)
  7. Operational excellence (monitoring and optimisation)

A ROAI alignment table maps how integration supports each quadrant.

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2. Current State Assessment and System Inventory

  1. Core systems inventory: Catalogue DMS, CRM, eBilling/financial, matter management, and email/communications platforms, including current integration points and constraints.
  2. AI BoM registration assessment: For each system, record whether AI components exist, are BoM-registered, and operate at Agentic Tier; register any missing entries before design proceeds.
  3. Class 6 Shadow AI audit: Identify ad-hoc or unapproved AI tools processing legal data. Any unregistered AI constitutes a Class 6 incident and must be escalated via STR-07 before continuing.
  4. Gap analysis: Document legacy limitations, API quality issues, security gaps, manual processes, data silos, unregistered AI tools, and absence of Agentic Tier controls.

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3. Target Architecture and Integration Design

3.1 Three-Tier Integration Architecture

  • Tier 1 — Core Legal Systems: DMS, CRM, eBilling, matter management, email and other data sources. Integrate via standardised, authenticated, monitored APIs. All data classified per DAT-02 before entering Tier 2.
  • Tier 2 — Integration Layer (Middleware): API gateway, message broker, data integration platform, service mesh, and AI BoM registry hook that validates all AI-to-system calls.
  • Tier 3 — AI Processing Infrastructure: GPAI platforms, legal-specific AI, enterprise AI services, and Agentic AI layer, all registered in the AI BoM.

3.2 API-First Integration Strategy

Design REST/GraphQL APIs with OpenAPI specs, versioning, and standard error handling. Use OAuth 2.0/OIDC, JWT, scoped API keys, certificate-based auth where required, and MFA for admin access. Implement rate limiting, circuit breakers, load balancing, and caching.

3.3 Event-Driven Architecture

Adopt publish-subscribe, request-reply, and queuing patterns. Define event categories: system, data, business, AI, and Class 6 events (unauthorised AI attempts). Use Kafka or cloud-native brokers for streaming and routing.

3.4 Data Synchronisation and Consistency

Apply real-time, near real-time, or scheduled sync based on criticality. Use strong consistency for financial/compliance data and eventual consistency for analytics. Enforce DAT-02 classification checks and enhanced controls for Level 3–4 data.

3.5 Agentic Tier Architecture Provisions

For any Level 4 (AI as Executor) tool:

  1. Scope-limited API access: Only the minimum systems and objects needed.
  2. Kill-switch API endpoint: Standardised endpoint that halts processing and revokes access within 60 seconds.
  3. Audit API: Complete, privilege-protected logs of actions, data accessed, and decisions.
  4. Intervention callback: Mechanism for attorneys to pause, review, and override actions.
  5. Cross-system data isolation: Matter-level partitioning to prevent cross-matter access.

No Agentic Tier integration goes live until all five provisions are documented and verified; exceptions require STR-07 approval.

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4. Security and Compliance Framework

4.1 Data Protection and Encryption

Use AES-256 and TDE for data at rest, encrypted backups, and HSM-backed key management. Enforce TLS 1.3, end-to-end encryption for Level 3–4 data, VPN tunnels, and message-level encryption for streams.

4.2 Access Controls and Identity Management

Require MFA for admin access, integrate with corporate IdPs, and apply RBAC aligned to legal roles with just-in-time elevation. Validate all AI API access against AI BoM entries.

4.3 Network Security and Segmentation

Implement DMZs, internal segmentation, a dedicated AI processing segment, micro-segmentation, IDS/IPS, DDoS protection, and zero-trust principles for remote access.

4.4 Regulatory Compliance

Map controls to ABA Model Rules (1.1, 1.4, 1.6, 5.3), GDPR (data minimisation, rights, DPIAs), EU AI Act (high-risk documentation, oversight, monitoring), and Risk Taxonomy 2026 classes.

4.5 Class 6 Shadow AI Detection

Defensibility Evidence

All integration architecture decision records, AI BoM alignment reports, Agentic Tier Architecture Gate confirmations, Class 6 Shadow AI incident records, security assessment reports, and vendor SLA verification records retained 7 years as DPS Defensibility lens evidence; ROAI dashboard reports and training completion records retained 5 years as DPS Adoption lens evidence.

Operational Artefacts

  • DAT-05 Integration Architecture Blueprint Workbook

    xlsx · v2026.1

    Gated
  • DAT-05 Integration Design and Testing Template

    docx · v2026.1

    Gated
  • DAT-05 Implementation and Governance Checklist

    checklist · v2026.1

Framework Crosswalk

NIST AI Risk Management Framework

NIST

Maps integration controls to AI RMF functions for governance, data management, and technical robustness.

ISO/IEC 42001 AI Management System

ISO

Supports AI management system requirements for lifecycle control, risk, and technical integration safeguards.

EU AI Act

European Union

Implements technical hooks for high-risk AI oversight, logging, and post-market monitoring via the integration layer.

GDPR

European Union

Enforces data minimisation, purpose limitation, security, and data subject rights at integration points.

ABA Model Rules of Professional Conduct

American Bar Association

Supports duties of competence, confidentiality, supervision, and client communication in AI-enabled workflows.

Operational Details

Inputs

  • · GOV-02 approved AI tools list
  • · AI BoM registered tool inventory
  • · DAT-01 Data Governance Framework
  • · STR-07 AI Task Force security requirements
  • · VEN-04 vendor security requirements and SLA specifications
  • · Current system inventory (DMS, CRM, eBilling, Matter Management, Email)
  • · Regulatory compliance requirements (GDPR, EU AI Act, ABA Model Rules, state bar rules)

Outputs

  • · Integration architecture documentation (three-tier design with Agentic Tier provisions)
  • · AI BoM alignment report confirming all integrated AI tools registered
  • · Class 6 Shadow AI Detection Architecture with 5-step STR-07 alerting pipeline
  • · Agentic Tier Architecture Gate confirmations for Level 4 AI-as-Executor tools
  • · ROAI implementation dashboard (Protect, Comply, Grow, Transform quadrants)
  • · System integration map with API patterns and data flow documentation
  • · Security and compliance framework documentation
  • · Phased implementation roadmap (4 phases)

Owner

Legal Operations + IT Architecture

Telemetry & Observability

Telemetry-ready

Key Takeaways

  • Establish a three-tier integration architecture linking core legal systems, middleware, and AI services.

  • Require AI BoM registration and Metric 0 gate checks before any integration work begins.

  • Use an API-first, event-driven approach with strong authentication, rate limiting, and monitoring.

  • Embed Class 6 Shadow AI detection and blocking at the API gateway, DNS, and DLP layers.

  • Apply five mandatory Agentic Tier controls before granting Level 4 AI tools execution access.

  • Align integration design with Risk Taxonomy 2026, GDPR, EU AI Act, and ABA Model Rules.

  • Retain integration artefacts and logs as DPS Defensibility evidence for at least seven years.

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Module Details

Type

Pillar

P2

Duration

6–12 months for initial rollout; 1–2 weeks for annual review and major change assessments.

Advisory

Yes

Access

Member access

Certification

Practitioner

Maturity Bands

FoundationalOperationalIntegratedOptimisedDefensible

Available Through

Governance

Methodology
v2026.1
Last reviewed
23 May 2026
Verified
23 May 2026

ADVISORY

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