Self Adapting Evidence Knowledge Graph for Real Time Compliance

In the fast‑moving world of SaaS, security questionnaires, audit requests, and regulatory checklists appear almost daily. Companies that rely on manual copy‑and‑paste workflows spend countless hours hunting for the right clause, confirming its validity, and tracking every change. The result is a brittle process that is prone to errors, version drift, and regulatory risk.

Enter the Self Adapting Evidence Knowledge Graph (SAEKG) – a living, AI‑enhanced repository that links every compliance artifact (policies, controls, evidence files, audit results, and system configurations) into a single graph. By continuously ingesting updates from source systems and applying contextual reasoning, SAEKG guarantees that the answers displayed in any security questionnaire are always consistent with the most recent evidence.

In this article we will:

1. Explain the core components of a self‑adapting evidence graph.
2. Show how it integrates with existing tools (Ticketing, CI/CD, GRC platforms).
3. Detail the AI pipelines that keep the graph in sync.
4. Walk through a realistic end‑to‑end scenario using Procurize.
5. Discuss security, auditability, and scalability considerations.

TL;DR: A dynamic knowledge graph powered by generative AI and change‑detection pipelines can turn your compliance docs into a single source of truth that updates questionnaire answers in real time.

1. Why a Static Repository Is Not Enough

Traditional compliance repositories treat policies, evidence, and questionnaire templates as static files. When a policy is revised, the repository gets a new version, but the downstream questionnaire answers stay unchanged until a human remembers to edit them. This gap creates three major problems:

A self‑adapting graph resolves these issues by binding each answer to a live node that points to the latest validated evidence.

2. Core Architecture of SAEKG

Below is a high‑level mermaid diagram that visualizes the main components and data flows.

  graph LR
    subgraph "Ingestion Layer"
        A["\"Policy Docs\""]
        B["\"Control Catalog\""]
        C["\"System Config Snapshots\""]
        D["\"Audit Findings\""]
        E["\"Ticketing / Issue Tracker\""]
    end

    subgraph "Processing Engine"
        F["\"Change Detector\""]
        G["\"Semantic Normalizer\""]
        H["\"Evidence Enricher\""]
        I["\"Graph Updater\""]
    end

    subgraph "Knowledge Graph"
        K["\"Evidence Nodes\""]
        L["\"Questionnaire Answer Nodes\""]
        M["\"Policy Nodes\""]
        N["\"Risk & Impact Nodes\""]
    end

    subgraph "AI Services"
        O["\"LLM Answer Generator\""]
        P["\"Validation Classifier\""]
        Q["\"Compliance Reasoner\""]
    end

    subgraph "Export / Consumption"
        R["\"Procurize UI\""]
        S["\"API / SDK\""]
        T["\"CI/CD Hook\""]
    end

    A --> F
    B --> F
    C --> F
    D --> F
    E --> F
    F --> G --> H --> I
    I --> K
    I --> L
    I --> M
    I --> N
    K --> O
    L --> O
    O --> P --> Q
    Q --> L
    L --> R
    L --> S
    L --> T

2.1 Ingestion Layer

• Policy Docs – PDFs, Markdown files, or repository‑stored policy‑as‑code.
• Control Catalog – Structured controls (e.g., NIST, ISO 27001 ) stored in a database.
• System Config Snapshots – Automated exports from cloud infra (Terraform state, CloudTrail logs).
• Audit Findings – JSON or CSV exports from audit platforms (e.g., Archer, ServiceNow GRC).
• Ticketing / Issue Tracker – Events from Jira, GitHub Issues that affect compliance (e.g., remediation tickets).

2.2 Processing Engine

• Change Detector – Uses diffs, hash comparison, and semantic similarity to identify what actually changed.
• Semantic Normalizer – Maps varying terminology (e.g., “encryption at rest” vs “data‑at‑rest encryption”) to a canonical form via a lightweight LLM.
• Evidence Enricher – Retrieves metadata (author, timestamp, reviewer) and attaches cryptographic hashes for integrity.
• Graph Updater – Adds/updates nodes and edges in the Neo4j‑compatible graph store.

2.3 AI Services

• LLM Answer Generator – When a questionnaire requests “Describe your data‑encryption process”, the LLM composes a concise answer from linked policy nodes.
• Validation Classifier – A supervised model that flags generated answers that deviate from compliance language standards.
• Compliance Reasoner – Runs rule‑based inference (e.g., if “Policy X” is active → answer must reference control “C‑1.2”).

2.4 Export / Consumption

The graph is exposed through:

• Procurize UI – Real‑time view of answers, with traceability links to evidence nodes.
• API / SDK – Programmatic retrieval for downstream tools (e.g., contract management systems).
• CI/CD Hook – Automated checks that ensure new code releases do not break compliance assertions.

3. AI‑Driven Continuous Learning Pipelines

A static graph would quickly become outdated. The self‑adapting nature of SAEKG is achieved through three looping pipelines:

3.1 Observation → Diff → Update

1. Observation: Scheduler pulls the latest artifacts (policy repo commit, config export).
2. Diff: A text‑diff algorithm combined with sentence‑level embeddings computes semantic change scores.
3. Update: Nodes whose change score exceeds a threshold trigger a re‑generation of dependent answers.

3.2 Feedback Loop from Auditors

When auditors comment on an answer (e.g., “Please include the latest SOC 2 report reference”), the comment is ingested as a feedback edge. A reinforcement‑learning agent updates the LLM prompting strategy to better satisfy future similar requests.

3.3 Drift Detection

Statistical drift monitors the distribution of LLM confidence scores. Sudden drops trigger a human‑in‑the‑loop review, ensuring the system never silently degrades.

4. End‑to‑End Walkthrough with Procurize

Scenario: A new SOC 2 Type 2 report is uploaded

1. Upload Event: Security team drops the PDF into the “SOC 2 Reports” folder in SharePoint. A webhook notifies the Ingestion Layer.
2. Change Detection: The Change Detector computes that the report version changed from v2024.05 to v2025.02.
3. Normalization: The Semantic Normalizer extracts relevant controls (e.g., CC6.1, CC7.2) and maps them to the internal control catalog.
4. Graph Update: New evidence nodes (Evidence: SOC2-2025.02) are linked to the corresponding policy nodes.
5. Answer Regeneration: The LLM re‑generates the answer for the questionnaire item “Provide evidence of your monitoring controls.” The answer now embeds a link to the new SOC 2 report.
6. Automatic Notification: The responsible compliance analyst receives a Slack message: “Answer for ‘Monitoring Controls’ updated to reference SOC2‑2025.02.”
7. Audit Trail: The UI shows a timeline: 2025‑10‑18 – SOC2‑2025.02 uploaded → answer regenerated → approved by Jane D.

All of this happens without the analyst opening the questionnaire manually, cutting the response cycle from 3 days to under 30 minutes.

5. Security, Auditable Trail, and Governance

5.1 Immutable Provenance

Every node carries:

• Cryptographic hash of the source artifact.
• Digital signature of the author (PKI‑based).
• Version number and timestamp.

These attributes enable a tamper‑evident audit log that satisfies SOC 2 and ISO 27001 criteria.

5.2 Role‑Based Access Control (RBAC)

Graph queries are mediated by an ACL engine:

5.3 GDPR & Data Residency

Sensitive personal data never leaves its source system. The graph stores only metadata and hashes, while the actual documents remain in the originating storage bucket (e.g., EU‑based Azure Blob). This design aligns with data minimization principles mandated by GDPR.

6. Scaling to Thousands of Questionnaires

A large SaaS provider may handle 10 k+ questionnaire instances per quarter. To keep latency low:

• Horizontal Graph Sharding: Partition by business unit or region.
• Cache Layer: Frequently accessed answer sub‑graphs cached in Redis with TTL = 5 min.
• Batch Update Mode: Nightly bulk diffs process low‑priority artifacts without affecting real‑time queries.

Benchmarks from a pilot at a mid‑size fintech (5 k users) showed:

• Average answer retrieval: 120 ms (95 th percentile).
• Peak ingestion rate: 250 documents/minute with < 5 % CPU overhead.

7. Implementation Checklist for Teams

8. Future Directions

1. Zero‑Knowledge Proofs for Evidence Validation – Prove that a piece of evidence satisfies a control without exposing the raw document.
2. Federated Knowledge Graphs – Allow partners to contribute to a shared compliance graph while preserving data sovereignty.
3. Generative RAG with Retrieval‑Augmented Generation – Combine graph search with LLM generation for richer, context‑aware answers.

The self‑adapting evidence knowledge graph is not a “nice‑to‑have” addition; it is becoming the operational backbone for any organization that wants to scale security questionnaire automation without sacrificing accuracy or auditability.

## See Also

• NIST SP 800‑53 Revision 5 – Control Catalog Overview
• ISO/IEC 27001:2022 – Information Security Management System