Solution
Protect Your IP. Observe Every Data Flow.
As LLMs and RAG architectures scale, the primary governance risk is data exfiltration. Beacon observes the technical intersection of your enterprise data and your AI systems.
What is Sensitive Data Exposure?
Sensitive Data Exposure in the AI context refers to situations where proprietary IP, customer PII, or internal datasets are ingested by, or processed through, AI models without proper authorization or human oversight. Beacon's platform observes these data flows in real-time, synthesizing Governance Situations when technical deltas indicate a potential violation of data residency or privacy policies.
"The transition from periodic compliance audits to continuous regulatory intelligence is the single biggest leverage point for AI and biopharma innovators today."
Q.How does Beacon detect data exposure without inspecting the data?
Direct Answer
Beacon is observer-first and stateless. We do not inspect the contents of your data. Instead, we observe the metadata and telemetry of the data/model intersection—such as unauthorized RAG connections, unmanaged API calls to public LLMs, and shifts in dataset access lineage.
Explanation
By focusing on the 'Technical Identity' of the data source and the 'Behavioral Profile' of the model, Beacon can identify high-risk exposure situations without ever needing to store or process sensitive information.
Real-World Example
"Beacon identified a situation where a developer connected a production support bot to an internal 'Customer_Churn_2026' database that had not been cleared for third-party LLM processing."
The Integrity of Data
IP Protection
Prevent the accidental leak of proprietary code or trade secrets into public model training sets by observing unmanaged RAG configurations.
Privacy Guardrails
Maintain absolute compliance with GDPR and HIPAA by identifying when PII enters an AI pipeline that lacks the required oversight controls.
Immediate Context
Every data exposure situation is automatically mapped to its business impact, allowing you to prioritize high-value IP protection.
Observed Flows vs. Manual Data Maps
| Category | Legacy / Manual | Beacon Intelligence |
|---|---|---|
| Discovery Method | Manual data mapping | Continuous metadata observation |
| Update Frequency | Quarterly reviews | Real-time intersection analysis |
| Risk Awareness | Self-reported (High error) | Evidence-backed (Low error) |
| Output | Privacy Gap Report | Governance Situation Brief |
The Data Pipeline
Endpoint Discovery
Beacon identifies every AI endpoint and its associated data connection strings.
Intersection Analysis
We analyze the metadata flow between your internal databases and model telemetry.
Policy Validation
Beacon cross-references the data connection against your internal data residency and privacy policies.
Situation Synthesis
Material data exposure risks are birthed as Situations with a full chain of evidence.
Synthesis Engine Active
Enterprise Situations
Unauthorized RAG Connection
Identifying when a Retrieval Augmented Generation stack is connected to a non-cleared data source.
Shadow Data Exfiltration
Detecting unmanaged API traffic patterns that indicate bulk data uploads to public LLMs.
Frequently Asked Questions
Q: Do you store our data?
A: No. Beacon is stateless. we ingest metadata and telemetry, synthesize understanding, and then discard the signals. We never store your model weights or datasets.
Q: Can you detect PII?
A: We identify the *situations* where PII is likely to be exposed based on technical identity and metadata, rather than performing deep packet inspection.
Q: How do you integrate with OneTrust?
A: Beacon is an upstream layer. When a data situation is synthesized, we trigger an incident remediation workflow in your OneTrust privacy module.
Q: Is this a DLP tool?
A: No. Data Loss Prevention (DLP) tools look for technical leaks. Beacon looks for *Governance Deviations*—situations where the technical reality drifts from your policy assumptions.