C-D

Abstraction - Operations

Summary

C to D: Output classification, clearance tiers, and redaction actions for each AI response provided to DLP, SIEM, and disclosure logs.

D to C: Output DLP violations, disclosure anomalies, and abuse patterns used to refine abstraction schemas, redaction rules, and seal-break handling in Pillar C, alongside D’s technical feedback to Pillar A.

Commons DraftEditorial research

Standards and Specifications

OpenTelemetry
OWASP LLM Top 10

This interface exposes how AI outputs are classified, redacted, and revealed so that security operations can monitor disclosure risk and provide feedback that improves abstraction logic over time. Pillar C must annotate responses with metadata such as sensitivity labels, clearance tiers applied, and which components were removed or transformed, enabling downstream tools to distinguish compliant from noncompliant disclosures. In the opposite direction, Pillar D must report when DLP or human review finds outputs that violate policy, leak sensitive training data, or exhibit harmful behavior, so that abstraction schemas, filters, and model usage patterns can be updated in Pillar C, while systemic issues flow into Pillar A via the A-D interface. A mature C-D interface ensures that output governance is observable, testable, and continuously adapted to real-world behavior and attack techniques like prompt injection and response manipulation.

Variants

Output metadata and classification feed to DLP and SIEM

Abstraction services attach structured metadata to each response—such as content classification, detected PII categories, applied clearance tier, and redaction status—that is logged and ingested by DLP and SIEM systems.

Requires a shared schema for output classification and redaction indicators so that multiple model endpoints and applications can be monitored consistently; OpenTelemetry traces can be extended with abstraction-specific attributes for correlation.

Inline DLP and policy checks at response time

Before final delivery, Pillar C passes candidate outputs through inline DLP and policy engines that may block, redact, or annotate content based on Pillar A rules and configured risk thresholds.

Tightens control but demands low-latency integrations and careful configuration to avoid overblocking; requires harmonized rule sets between inline checks and downstream monitoring so that decisions are explainable and consistent.

Operations-driven refinement of abstraction schemas

Pillar D aggregates disclosure incidents and near misses, then collaborates with Pillar C to adjust output schemas, redaction routines, and template structures that better contain sensitive content.

Works best when abstraction behavior is defined in structured configuration or code rather than ad hoc prompts, allowing specific fields and templates to be updated in response to operational findings; change management should tie schema versions to incident trends.

Seal-break event logging and review

When Pillar C intentionally overrides or escalates clearance rules—for example under emergency access or human override—it logs a seal-break event that operations can review and correlate with downstream impact.

Requires explicit representation of seal-break conditions in both abstraction logic and logging semantics, and a clear process in operations for reviewing and, if necessary, escalating such events to governance.

OWASP LLM Top 10 abuse detection feedback

Operations tools detect output patterns indicating jailbreaks, data exfiltration, or harmful content as described in OWASP LLM Top 10, and share structured signatures and examples with Pillar C to adjust prompts, system messages, and post-processing filters.

Depends on capturing enough context for pattern analysis while preserving privacy, and on standardizing categories and signature formats so that multiple abstraction components can adopt improved defenses efficiently.

Participating Vendors

LangChain

LangChain is an AI orchestration framework operating across Pillars A, B, C, and D, integrating with policy engines (OPA, Cerbos) for pre-retrieval authorization in Pillar B, output filtering in Pillar C, and emitting structured trace logs to Pillar D SIEM for audit and anomaly detection.

Guardrails AI

Guardrails AI provides LLM output filtering in Pillars C and D, enforcing clearance-tier aware abstraction policies and detecting policy violations in AI outputs. Emits structured policy violation events to Pillar D SIEM for compliance monitoring.