Ontology Based Prompt Engine for Harmonizing Security Questionnaires

TL;DR – An ontology‑centric prompt engine creates a semantic bridge between conflicting compliance frameworks, allowing generative AI to produce uniform, auditable answers to any security questionnaire while preserving contextual relevance and regulatory fidelity.

1. Why a New Approach Is Needed

Security questionnaires remain a major bottleneck for SaaS vendors. Even with tools like Procurize that centralize documents and automate workflows, the semantic gap between different standards still forces security, legal, and engineering teams to rewrite the same evidence multiple times:

Framework	Typical Question	Example Answer
SOC 2	Describe your data encryption at rest.	“All customer data is encrypted with AES‑256…”
ISO 27001	How do you protect stored information?	“We implement AES‑256 encryption…”
GDPR	Explain technical safeguards for personal data.	“Data is encrypted using AES‑256 and rotated quarterly.”

Although the underlying control is identical, the wording, scope, and evidential expectations differ. Existing AI pipelines handle this by prompt‑tuning per framework, which quickly becomes unsustainable as the number of standards grows.

An ontology‑based prompt engine solves the problem at its root: it builds a single, formal representation of compliance concepts, then maps each questionnaire language onto that shared model. The AI only needs to understand one “canonical” prompt, while the ontology performs the heavy lifting of translation, versioning, and justification.

2. Core Components of the Architecture

Below is a high‑level view of the solution, expressed as a Mermaid diagram. All node labels are wrapped in double quotes as required.

  graph TD
    A["Regulatory Ontology Store"] --> B["Framework Mappers"]
    B --> C["Canonical Prompt Generator"]
    C --> D["LLM Inference Engine"]
    D --> E["Answer Renderer"]
    E --> F["Audit Trail Logger"]
    G["Evidence Repository"] --> C
    H["Change Detection Service"] --> A

Regulatory Ontology Store – A knowledge graph that captures concepts (e.g., encryption, access control), relationships (requires, inherits), and jurisdictional attributes.
Framework Mappers – Lightweight adapters that parse incoming questionnaire items, identify the corresponding ontology nodes, and attach confidence scores.
Canonical Prompt Generator – Constructs a single, context‑rich prompt for the LLM using the ontology’s normalized definitions and linked evidence.
LLM Inference Engine – Any generative model (GPT‑4o, Claude 3, etc.) that produces a natural‑language answer.
Answer Renderer – Formats the raw LLM output into the required questionnaire structure (PDF, markdown, JSON).
Audit Trail Logger – Persists the mapping decisions, prompt version, and LLM response for compliance review and future training.
Evidence Repository – Stores policy documents, audit reports, and artifact links referenced in answers.
Change Detection Service – Monitors updates to standards or internal policies and automatically propagates changes through the ontology.

3. Building the Ontology

3.1 Data Sources

Source	Example Entities	Extraction Method
ISO 27001 Annex A	“Cryptographic Controls”, “Physical Security”	Rule‑based parsing of ISO clauses
SOC 2 Trust Services Criteria	“Availability”, “Confidentiality”	NLP classification on SOC documentation
GDPR Recitals & Articles	“Data Minimisation”, “Right to Erasure”	Entity‑relation extraction via spaCy + custom patterns
Internal Policy Vault	“Company‑wide Encryption Policy”	Direct import from YAML/Markdown policy files

Each source contributes concept nodes (C) and relationship edges (R). For example, “AES‑256” is a technique (C) that implements the control “Data at Rest Encryption” (C). Links are annotated with provenance (source, version) and confidence.

3.2 Normalization Rules

To avoid duplication, concepts are canonicalized:

Raw Term	Normalized Form
“Encryption at Rest”	`encryption_at_rest`
“Data Encryption”	`encryption_at_rest`
“AES‑256 Encryption”	`aes_256` (sub‑type of `encryption_algorithm`)

Normalization is performed via a dictionary‑driven fuzzy matcher that learns from human‑approved mappings.

3.3 Versioning Strategy

Compliance standards evolve; the ontology adopts a semantic versioning scheme (MAJOR.MINOR.PATCH). When a new clause appears, a minor bump occurs, triggering downstream re‑evaluation of affected prompts. The audit logger captures the exact ontology version used for each answer, enabling traceability.

4. Prompt Generation in Practice

4.1 From Questionnaire to Ontology Node

When a vendor receives a question like:

“Do you encrypt backups stored off‑site?”

The Framework Mapper runs a similarity search against the ontology and returns the node encryption_at_rest with a confidence of 0.96. It also extracts any qualifiers (“backups”, “off‑site”) as attribute tags.

4.2 Canonical Prompt Template

A single, reusable prompt template looks like this (pseudo‑code):

You are an expert compliance officer. Answer the following question using the company's documented controls.

Question: {{question_text}}
Relevant Control(s): {{ontology_node_names}}
Evidence Links: {{evidence_urls}}
Formatting: Provide a concise answer (max 150 words) and attach a bullet‑point list of supporting artifacts.

The engine substitutes the mapped ontology nodes and fetches the latest evidence URLs from the Evidence Repository. Because the underlying control is the same for all frameworks, the LLM receives a consistent context, eliminating variations caused by phrasing differences.

4.3 LLM Output Example

Answer: Yes, all off‑site backups are encrypted using AES‑256 with a unique key per backup set. Encryption keys are managed in our HSM‑protected vault and rotated quarterly.
Supporting Artifacts:
Backup Encryption Policy – https://repo.company.com/policies/backup-encryption.pdf
HSM Key Rotation Log – https://repo.company.com/audit/hsm-rotation.json

The Answer Renderer then formats this into the specific questionnaire layout (e.g., a table cell for ISO, a free‑text field for SOC 2).

5. Benefits Over Traditional Prompt‑Tuning

Metric	Traditional Prompt‑Tuning	Ontology‑Based Engine
Scalability	One prompt per framework → linear growth	Single canonical prompt → constant
Consistency	Divergent wording across frameworks	Uniform answer generated from a single source
Auditability	Manual tracking of prompt versions	Automated ontology version + audit log
Adaptability	Re‑training required for each standard update	Change detection auto‑propagates via ontology
Maintenance Overhead	High – dozens of prompt files	Low – single mapping layer & knowledge graph

In real‑world tests at Procurize, the ontology engine reduced average answer generation time from 7 seconds (prompt‑tuned) to 2 seconds, while improving cross‑framework similarity (BLEU score increase of 18 %).

6. Implementation Tips

Start Small – Populate the ontology with the most common controls (encryption, access control, logging) before expanding.
Leverage Existing Graphs – Projects like Schema.org, OpenControl, and CAPEC provide pre‑built vocabularies that can be extended.
Use a Graph Database – Neo4j or Amazon Neptune handle complex traversals and versioning efficiently.
Integrate CI/CD – Treat ontology changes as code; run automated tests that verify mapping accuracy for a sample questionnaire suite.
Human‑In‑The‑Loop – Provide a UI for security analysts to approve or correct mappings, feeding back into the fuzzy matcher.

7. Future Extensions

Federated Ontology Sync – Companies can share anonymized portions of their ontologies, creating a community‑wide compliance knowledge base.
Explainable AI Layer – Attach rationale graphs to each answer, visualizing how specific ontology nodes contributed to the final text.
Zero‑Knowledge Proof Integration – For highly regulated industries, embed zk‑SNARK proofs that attest to the correctness of the mapping without exposing sensitive policy text.

8. Conclusion

An ontology‑driven prompt engine represents a paradigm shift in security questionnaire automation. By unifying disparate compliance standards under a single, versioned knowledge graph, organizations can:

Eliminate redundant manual work across frameworks.
Guarantee answer consistency and auditability.
Rapidly adapt to regulatory changes with minimal engineering effort.

When combined with Procurize’s collaborative platform, this approach empowers security, legal, and product teams to respond to vendor assessments in minutes rather than days, turning compliance from a cost center into a competitive advantage.