Four layers of layered detection
102 detection capabilities protecting against misdirection, data loss, and threats. On-device policy + DLP, on-prem ML/NLP, and cloud-based AI/LLM work together to secure every outbound email.
Four layers of defense
Our layered approach combines on-device and on-prem processing with cloud-based AI/LLM for context understanding. Each layer adds depth without compromising speed.
Policy Engine
47 on-device policies
Deterministic heuristics provide fast, predictable analysis with configurable thresholds.
DLP Engine
46+ detectors, 29+ countries
PII and financial data detection with regional compliance coverage worldwide.
ML/NLP Scanners
4 scanners
On-prem models detect context patterns invisible to rule-based systems.
AI/LLM Models
5 models
Deep contextual understanding for ambiguous cases with cloud-based AI/LLM analysis.
Protection at compose-time
WaverSec Protect runs before you click send—not after. Catch mistakes before they become incidents.
Compose
As you write your email, WaverSec analyzes recipients, content, and attachments in real-time.
Warning
Risks appear in the sidebar panel with severity levels and clear explanations of what was detected.
Fix or Send
One-click fixes to remove flagged recipients or attachments, or acknowledge the warning and send anyway.
Instant, predictable protection
47 deterministic policies provide fast, reliable analysis. No ML uncertainty—policies either match or they don't. Runs entirely on-device.
Misdirection Detection
- Greeting mismatches
- Wrong recipients
- Thread context issues
- Reply-all guards
Recipient Trust
- Typosquatting detection
- Freemail warnings
- Disposable emails
- Role-based addresses
Content Guards
- Legal privilege markers
- Confidential labels
- Compliance references
- Custom regex patterns
Sharing Controls
- External recipient flags
- Information barriers
- Distribution analysis
- BCC monitoring
Configurable: choose policies, tune severity levels, set thresholds per organization.
Global compliance coverage
46+ PII and financial data detectors with checksum validation. Coverage across 29+ countries—from SSNs to IBANs to national IDs.
6 detectors
Credit cards, phone, email, IP, crypto
6 detectors
SSN, ITIN, passport, driver license
18 detectors
IBAN, UK NINO, DE Steuer-ID, FR NIR
5 detectors
CA SIN, BR CPF, BR CNPJ, AR CUIL, MX CURP
11 detectors
AU TFN, SG NRIC, IN PAN, JP My Number
Financial Data
Credit cards, bank accounts, routing numbers
Identity Documents
SSN, passports, national IDs, driver licenses
Checksum Validation
Luhn, MOD-11, MOD-97, format validation
Context that heuristics can't catch
When deterministic policies aren't enough, ML/NLP scanners provide contextual analysis. 4 on-prem scanners with minimal context transmission and stateless processing.
Misdirection Scanner
Uses NER and topic extraction to detect when email content doesn't align with the recipient list.
Attachments Scanner
Analyzes attachment content and context to detect files being sent to wrong recipients.
Sentiment Scanner
Analyzes emotional tone to detect negative, aggressive, or unprofessional communication.
Single Recipient Scanner
Uses NER to detect when email content mentions people who are not the recipient.
On-prem processing: NER, topic extraction, sentiment, and misdirection analysis with minimal context and immediate discard.
Deep AI/LLM understanding when needed
For ambiguous cases requiring deeper reasoning, cloud-based AI/LLM models provide deeper analysis with admin control.
Email Misdirection Analysis
Identifies misdirection patterns and context mismatches that heuristics miss.
Attachment Misdirection Analysis
Detects wrong-file attachment patterns by analyzing context between email body and attachments.
Results Interpretation
Translates technical warnings into clear, actionable guidance for end users.
Email Tone Analysis
Detects inappropriate tone, profanity, and hostile language before emails are sent.
Attachment Tone Analysis
Detects unprofessional language in attachment filenames and content.
Privacy-first AI/LLM
AI/LLM models add advanced context analysis. When that layer is enabled, only the context needed for the analysis is sent to the configured provider. The cloud layer is optional and WaverSec Protect is designed to minimize retained message data.
Ready to see it in action?
Sign in to explore the full detection dashboard. Configure policies, enable detectors, and see how WaverSec Protect secures your outbound email.
FAQ
Detection Questions
Common questions about how WaverSec detection technology works.
How accurate is the detection?
The Policy Engine and DLP Engine use deterministic heuristics with checksum validation to reduce false positives. ML/NLP models are tuned for high precision, and cloud-based AI/LLM analysis provides explainable reasoning for edge cases.
Can I customize which policies are enabled?
Yes. Administrators choose which policies are active, adjust severity levels, and set custom thresholds. Available policies, DLP detectors, scanners, and AI models depend on your plan. The Business plan includes access to all 47 policies and every detection layer.
Does detection slow down email composition?
No. Policy and DLP run on-device and ML/NLP runs on-prem with sub-second latency. Analysis happens in real-time as you type, not when you click send.
What's the difference between the Policy Engine and ML/NLP layer?
The Policy Engine uses deterministic heuristics—explicit patterns that either match or don't. ML/NLP models analyze context and meaning, catching subtle mismatches (like discussing "Project Alpha" while emailing "Project Beta" team members) that heuristics can't detect.
What happens when the AI/LLM layer detects something?
When used, cloud-based AI/LLM models (third-party, tuned for WaverSec Protect) provide deeper contextual reasoning for ambiguous cases. Results appear in the warning panel with clear explanations under admin control.
What data does each layer analyze?
Each layer focuses on different aspects of your email. The Policy Engine checks recipient patterns and metadata. DLP scans content for PII and financial data. ML/NLP analyzes subject and body for entity and topic matching. Cloud-based AI/LLM models provide semantic understanding across all available context.
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