Continuous Compliance Monitor

Transforms human-readable regulatory requirements (GDPR, EU AI Act, SOC 2) into machine-executable rules. Instead of manual checklists, the engine parses structured policy definitions to validate configurations.

• Declarative Syntax: Uses languages like Rego (Open Policy Agent) to define 'desired state'.
• Drift Elimination: Instantly flags any divergence between the declared policy and the live environment.
• Version Control: Policies are stored in Git, enabling audit trails and rollback capabilities.

Continuously consumes high-cardinality event streams from across the entire infrastructure stack without batch processing delays.

• Signal Sources: Ingests logs, metrics, traces, and change events from Kubernetes, Terraform, and CI/CD pipelines.
• Stream Processing: Utilizes platforms like Kafka or Redpanda to handle millions of events per second.
• Contextual Enrichment: Automatically decorates raw telemetry with metadata (region, owner, data classification) for precise policy evaluation.

Moves beyond passive alerting to actively correct non-compliant configurations, closing the gap between detection and resolution.

• Self-Healing: Triggers a webhook or executes a pre-approved runbook to revert a misconfigured firewall rule or encryption setting.
• Idempotency Guarantees: Uses idempotency keys to ensure that retries of a remediation action do not cause cascading side effects.
• Circuit Breaker Integration: Pauses automated fixes if a downstream service becomes unhealthy, preventing a bad state from worsening.

Tracks the origin, movement, and transformation of every data element to verify that information used in decision-making has not been tampered with.

• Immutable Graph: Builds a directed acyclic graph (DAG) of data flow from source to consumption.
• C2PA Alignment: Validates tamper-evident metadata to ensure content authenticity.
• Anomaly Detection: Applies an out-of-distribution detector to lineage patterns, alerting if data suddenly flows to an unregistered sink.

Specifically monitors AI-generated content and model outputs to ensure they remain factually anchored and contextually relevant over time.

• Cosine Similarity Guard: Compares vector embeddings of generated text against a golden dataset, blocking output that falls below a similarity threshold.
• Entailment Check: Verifies that a generated hypothesis logically follows from the source premise, preventing factual fabrication.
• Calibration Scoring: Continuously measures the alignment between a model's confidence and its actual accuracy to detect silent degradation.

Ensures zero data loss and complete auditability when compliance checks fail or events cannot be processed.

• Persistent Storage: Non-compliant events are routed to a dead letter queue for forensic analysis, not discarded.
• Manual Intervention Trigger: Alerts a human operator with the full context of the failed event for complex judgment calls.
• Replay Capability: Allows operators to reprocess failed events after a policy update or bug fix, ensuring eventual consistency.

A threshold-based filter that compares vector embeddings of generated text against a reference source. It blocks output falling below a minimum semantic similarity score.

• Embedding Model: Converts text into high-dimensional vectors capturing semantic meaning
• Similarity Threshold: Typically set between 0.75 and 0.95 depending on strictness requirements
• Real-Time Blocking: Rejects non-compliant output before it reaches the user
• Example: A customer-facing chatbot must stay within 0.85 cosine similarity of approved support documentation; any deviation is silently replaced with a fallback response