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Data Protection
Sensitive DLP
Your AI interacts with your most sensitive business data every day. Aiceberg understands what's sensitive, why it's sensitive, and automatically enforces your policy — without months of manual data labeling.
DLP That Understands Your Data
Traditional Data Loss Prevention relies on pattern matching — scanning for credit card numbers, social security formats, or keywords you've manually defined. That works for structured data. But when employees share business strategy, financial projections, legal opinions, or HR discussions through AI, pattern matching is blind.
Aiceberg Sensitive DLP goes beyond patterns. It processes content through a hierarchy of specialized models that understand whether something is business-related, which domain it belongs to, what topics are present, and exactly how sensitive that combination is — all in real time, all without requiring you to label a single document.
Sensitive DLP — Live Analysis
Analyzing
Outbound AI Response
"Based on the quarterly review, revenue is projected at $42M with a margin compression to 18% due to the pending acquisition of Nextera Corp. CFO Sarah Mitchell has approved the revised forecast."
1
Business Content?
Yes — Business
2
Domain Classification
Finance
3
Topic Detection
M&A · Revenue · Forecast
4
Sensitive Elements
Named Exec · Financials · Target
5
Sensitivity Grade
Level 4 — Critical
Why Traditional DLP Falls Short for AI
Existing approaches were built for structured data and known patterns — not for the fluid, contextual nature of AI conversations.
Manual Data Labeling
Traditional DLP requires you to classify and label your data before it can be protected. For AI interactions — which generate novel content in real time — this is an impossible task.
Months of effort. Constant maintenance. Always incomplete.
Pattern-Only Detection
Regex and keyword rules catch credit card numbers and SSNs. But they can't detect when an employee shares a revenue forecast, a legal opinion, or an acquisition target through AI.
Blind to contextual sensitivity. Misses the data that matters most.
Volume & Velocity
AI interactions are high-volume, real-time, and conversational. Traditional DLP workflows — with manual review queues and delayed enforcement — simply can't keep up.
By the time you review it, the data has already left the building.
The Aiceberg Approach
Instead of asking you to label your data, Aiceberg understands it. Content flows through a hierarchy of specialized models — each one narrowing the classification until sensitivity is precisely graded.
1
Business Content Detection
Is this content business-related or general conversation? Filters out noise so only relevant content is analyzed further.
Business / General
Binary classification
2
Domain Classification
Which business domain does the content belong to? HR, Finance, Legal, Engineering, Sales, Executive — each domain has different sensitivity thresholds.
Domain identified
Multi-class classification
3
Topic Detection
What specific topics are present? Compensation, M&A activity, litigation strategy, product roadmap, customer data — granular topic classification within the domain.
Topics extracted
Multi-label classification
4
Sensitive Element Extraction
What specific sensitive information is present for this domain and topic combination? Named executives, financial figures, deal terms, employee records.
Elements identified
Entity extraction
5
Sensitivity Grading
Based on domain, topic, and elements detected — assign a sensitivity score from 0 (public) to 4 (critical). This score drives your enforcement policy.
Score 0–4
Policy enforcement
Sensitivity Scale
✎ Auto-Redact
Sensitive elements are automatically replaced before content reaches the AI model or the end user. The interaction continues — minus the sensitive parts.
"CFO Sarah Mitchell [EXECUTIVE] has approved the $42M revenue forecast [FINANCIAL FIGURE] for Q3, reflecting margin compression to 18% [FINANCIAL METRIC]."
⊘ Block
When content exceeds your sensitivity threshold, the entire interaction is blocked. The user receives a clear explanation of why and what policy was triggered.
⊘ Blocked — Content contains Level 4 sensitive data (M&A: acquisition target identified). Policy: Finance-Critical requires VP approval.