Контекстно‑Залежне Адаптивне Генерування Підказок для Багатофреймворкових Безпекових Анкет

Анотація
Enterprises today juggle dozens of security frameworks—SOC 2, ISO 27001, NIST CSF, PCI‑DSS, GDPR, and many more. Each framework poses a unique set of questionnaires that security, legal, and product teams must answer before a single vendor deal can close. Traditional methods rely on manually copying answers from static policy repositories, which leads to version drift, duplicated effort, and increased risk of non‑compliant responses.

Procurize AI introduces Context‑Aware Adaptive Prompt Generation (CAAPG), a generative‑engine‑optimized layer that automatically crafts the perfect prompt for any questionnaire item, taking into account the specific regulatory context, the maturity of the organization’s controls, and real‑time evidence availability. By combining a semantic knowledge graph, a retrieval‑augmented generation (RAG) pipeline, and a lightweight reinforcement‑learning (RL) loop, CAAPG delivers answers that are not only faster but also auditable and explainable.

1. Чому Генерування Підказок Має Значення

The core limitation of large language models (LLMs) in compliance automation is prompt brittleness. A generic prompt such as “Explain our data‑encryption policy” can produce a response that is too vague for a SOC 2 Type II questionnaire but overly detailed for a GDPR data‑processing addendum. The mismatch creates two problems:

1. Inconsistent language across frameworks, weakening the perceived maturity of the organization.
2. Increased manual editing, which re‑introduces the very overhead the automation intended to eliminate.

Adaptive prompting solves both issues by conditioning the LLM on a concise, framework‑specific instruction set. The instruction set is derived automatically from the questionnaire’s taxonomy and the organization’s evidence graph.

2. Архітектурний Огляд

Below is a high‑level view of the CAAPG pipeline. The diagram uses Mermaid syntax to stay within the Hugo Markdown ecosystem.

  graph TD
    Q[Questionnaire Item] -->|Parse| T[Taxonomy Extractor]
    T -->|Map to| F[Framework Ontology]
    F -->|Lookup| K[Contextual Knowledge Graph]
    K -->|Score| S[Relevance Scorer]
    S -->|Select| E[Evidence Snapshot]
    E -->|Feed| P[Prompt Composer]
    P -->|Generate| R[LLM Answer]
    R -->|Validate| V[Human‑in‑the‑Loop Review]
    V -->|Feedback| L[RL Optimizer]
    L -->|Update| K

Key components

3. Від Питання до Підказки – Крок за Кроком

3.1 Витяг Таксономії

A questionnaire item is first tokenized and passed through a lightweight BERT‑based classifier trained on a corpus of 30 k security‑question examples. The classifier outputs a hierarchical tag list:

Item: “Do you encrypt data at rest using industry‑standard algorithms?”
Tags: [Data Protection, Encryption, At Rest, AES‑256]

3.2 Відображення на Онтологію

Each tag is cross‑referenced with the Framework Ontology. For SOC 2 the tag “Encryption at Rest” maps to the Trust Services Criteria CC6.1; for ISO 27001 it maps to A.10.1. This mapping is stored as a bidirectional edge in the KG.

3.3 Оцінка Знань у Графі

The KG contains nodes for actual policies (Policy:EncryptionAtRest) and evidence artifacts (Artifact:KMSKeyRotationLog). A GraphSAGE model computes a relevance vector for each node given the taxonomy tags, returning a ranked list:

1. Policy:EncryptionAtRest
2. Artifact:KMSKeyRotationLog (last 30 days)
3. Policy:KeyManagementProcedures

3.4 Формування Підказки

The Prompt Composer concatenates the top‑K nodes into a structured instruction:

[Framework: SOC2, Criterion: CC6.1]
Use the latest KMS key rotation log (30 days) and the documented EncryptionAtRest policy to answer:
“Describe how your organization encrypts data at rest, specifying algorithms, key management, and compliance controls.”

Notice the contextual markers ([Framework: SOC2, Criterion: CC6.1]) that guide the LLM to produce framework‑specific language.

3.5 Генерація LLM та Валідація

The composed prompt is sent to a fine‑tuned, domain‑specific LLM (e.g., GPT‑4‑Turbo with a compliance‑focused instruction set). The raw answer is then sent to a Human‑in‑the‑Loop (HITL) reviewer. The reviewer can:

• Accept the answer.
• Provide a brief correction (e.g., replace “AES‑256” with “AES‑256‑GCM”).
• Flag missing evidence.

Each reviewer action is logged as a feedback token for the RL optimizer.

3.6 Цикл Підкріплювального Навчання

A Proximal Policy Optimization (PPO) agent updates the prompt‑generation policy to maximize the acceptance rate and minimize the editing distance. Over weeks, the system converges to prompts that produce near‑perfect answers straight out of the LLM.

4. Переваги, Підтверджені Реальними Метриками

These numbers come from a pilot with a SaaS provider handling 150 vendor questionnaires per quarter across 8 frameworks.

5. Пояснювальна Здатність та Аудит

Compliance officers often ask, “Why did the AI choose this wording?” CAAPG addresses this with traceable prompt logs:

1. Prompt ID: Unique hash for each generated prompt.
2. Source Nodes: List of KG node IDs used.
3. Scoring Log: Relevance scores for each node.
4. Reviewer Feedback: Timestamped correction data.

All logs are stored in an immutable Append‑Only Log (leveraging a lightweight blockchain variant). The audit UI surfaces a Prompt Explorer where an auditor can click any answer and instantly view its provenance.

6. Безпека та Приватність

Because the system ingests sensitive evidence (e.g., encryption keys logs), we enforce:

• Zero‑Knowledge Proofs for evidence validation—proving that a log exists without exposing its contents.
• Confidential Computing (Intel SGX enclaves) for the KG scoring stage.
• Differential Privacy when aggregating usage metrics for the RL loop, ensuring no individual questionnaire is reverse‑engineered.

7. Додавання Нових Фреймворків у CAAPG

Adding a new compliance framework is straightforward:

1. Upload Ontology CSV mapping framework clauses to universal tags.
2. Run the taxonomy‑to‑ontology mapper to generate KG edges.
3. Fine‑tune the GNN on a small set of labeled items (≈500) from the new framework.
4. Deploy – CAAPG automatically begins generating context‑aware prompts for the new questionnaire set.

The modular design means that even niche frameworks (e.g., FedRAMP Moderate or CMMC) can be onboarded within a week.

8. Майбутні Напрямки

Procurize’s roadmap includes a beta of Federated Knowledge Graph Collaboration, which will let suppliers and customers exchange compliance context while preserving confidentiality.

9. Як Розпочати Роботу з CAAPG у Procurize

1. Activate the “Adaptive Prompt Engine” in the platform settings.
2. Connect your Evidence Store (e.g., S3 bucket, Azure Blob, internal CMDB).
3. Import your Framework Ontologies (CSV template available in the Docs).
4. Run the “Initial KG Build” wizard – it will ingest policies, controls, and artifacts.
5. Assign a “Prompt Reviewer” role to one security analyst for the first two weeks to collect feedback.
6. Monitor the “Prompt Acceptance Dashboard” to watch the RL loop improve performance.

Within a single sprint, most teams see a 50 % reduction in questionnaire turnaround time.

10. Висновок

Context‑Aware Adaptive Prompt Generation reframes the security questionnaire problem from manual copy‑paste to dynamic, AI‑driven conversation. By anchoring LLM output in a semantic knowledge graph, grounding prompts in framework‑specific ontologies, and continuously learning from human feedback, Procurize delivers:

• Speed – answers in seconds, not minutes.
• Accuracy – evidence‑linked, framework‑compliant text.
• Auditability – full provenance for every generated response.
• Scalability – seamless onboarding of new regulations.

Enterprises that adopt CAAPG can close vendor deals faster, lower compliance staffing costs, and maintain a compliant posture that is provably linked to concrete evidence. For organizations already handling FedRAMP workloads, the built‑in support for FedRAMP controls ensures that even the most stringent federal requirements are met without additional engineering effort.