Sensitive information evaluation
Employees increasingly use commercial LLMs for productivity - transcribing, summaries, drafting, coding assistance, research. But these models receive raw enterprise data that may include confidential IP, regulated information, customer data, or internal strategy. Commercial AI providers apply generic, one-size-fits-all guardrails (toxicity, generic PII), which do not reflect an enterprise’s actual data sensitivity policies.
Enterprises need custom, deterministic, real-time evaluation of what employees send to external AI models - and of what those models return.
Orca provides that control layer.
The problem with current controls
No enterprise-specific guardrails
Your enterprise needs are unique and require specialization - internal confidentiality boundaries, regulated data types, strategic content categories, or internal taxonomy of sensitive material. One-size-fits-all PII filters are insufficient
Slow-to-update policies
If the business updates its confidentiality rules or defines a new sensitive topic (e.g., new product codename, acquisition discussions), updating internal filters or LLM prompts is slow and brittle. Training new classifiers takes days to weeks
LLM-based filters introduce latency & nondeterminism
LLMs used as judges add 100–1000ms latency and produce variable, hard-to-audit results - unacceptable for inline enterprise workflows
Poor handling of out-of-distribution inputs
Commercial models fail unpredictably when encountering unfamiliar, sensitive internal content - business plans, code words or jargon, architecture docs, or encoded identifiers - not present in public training sets
Orca's solution:
Enterprise-specific, real-time safe use of commercial AI
1. Ultra-low-latency classification for every prompt & response
Orca evaluates each transaction - what employees send to a commercial LLM and what comes back - in tens of milliseconds, enabling safe inline use at scale.
2. Full enterprise customization
Swap memorysets per request to enforce:
- Internal confidential data categories
- Regulated data patterns (finance, healthcare, legal)
- Department-specific policies
- Customer-specific restrictions
- Compliance requirements (SOC, HIPAA, FINRA, etc)
One model, many policies - no branching rules or model sprawl
3. Determinism and auditability
Evaluations link to explicit memory items, not probabilistic LLM judgments. This enables:Swap memorysets per request to enforce:
- Reproducible decisions
- Clear governance trails
- Customer-specific restrictions
4. Instant policy updates (no retraining)
If security updates a policy - e.g., “Block all content referring to Project Atlas” - Orca enforces it immediately by editing the memoryset.
No retraining. No exposure window.
5. Superior handling of out-of-distribution cases
Orca’s retrieval-augmented architecture adapts to new, previously unseen internal content by updating memorysets. This avoids the degradation that plagues static classifiers under drift.
Evaluations you can enforce inline
For both employee → LLM and LLM → employee flows:
- Sensitive internal topic detection
- Proprietary IP leakage detection
- Custom PII / regulated data patterns
- Contract / legal confidentiality constraints
- Brand and communication policy adherence
- Accuracy checks and escalation routing
- “Is this safe to send externally?” gating
- “Does this response violate internal policy?” checks
Example workflow
1. Employee enters a prompt to a commercial LLM
2. Orca evaluates the input for sensitive content based on enterprise-specific memorysets
3. If permitted, the prompt is forwarded to the external LLM
4. Orca evaluates the returned text for policy, accuracy, and compliance
5. Orca either passes the response through, corrects it, or escalates to a human/system workflow
Total additional latency: ~20–50ms
Where this is a fit
- Enterprises enabling employee access to commercial AI
- Security, compliance, and legal teams requiring deterministic, auditable control
- Organizations with frequent policy updates or complex confidentiality rules
- Environments with high risk of IP leakage or regulated data exposure
- Teams needing low-latency inline decisioning at scale
Talk to Orca
Speak to our engineering team to learn how we can help you unlock high performance agentic AI / LLM evaluation, real-time adaptive ML, and accelerated AI operations.