Encrypted by default
Most enterprise traffic is HTTPS. Payloads are opaque at scale, and decryption is often off the table for privacy and compliance.
Detect Threat Signals
within Encrypted Traffic
No decryption · Earlier signals · Better visibility
The Problem
Malware hides in TLS
Adversaries increasingly deliver malware over HTTPS/TLS to bypass legacy inspection.
Many modern attacks now operate over encrypted channels.
Slow detection hurts
On average, breaches still take months to uncover and contain, driving higher cost and impact.
≈87%
of threats delivered over encrypted channels
≈241
days to identify + contain a breach
Sources: Zscaler ThreatLabz Encrypted Attacks (2024); IBM Security — Cost of a Data Breach (2025).
Our Approach
AI behavior detection, not signatures
BlackCrypt uses AI and machine-learning models to learn behavioral patterns from encrypted-session metadata, surfacing suspicious encrypted-session behavior without relying on payload decryption or static signatures.
Earlier signal, faster containment
Behavioral scoring surfaces suspicious encrypted sessions earlier, helping analysts accelerate investigation and containment across captured and monitored traffic.
Metadata, not payloads
We analyze SSL/TLS handshakes, ciphers, timing and flows without decrypting payloads, keeping content out of scope while preserving security signal.
How It Works
Network Capture Input
PCAP Upload
Tap Stream
Feature Extraction
Metadata
SSL/TLS signals
BlackCrypt AI Engine
Behavioral threat detection for encrypted traffic
Results & Enrichment
Findings, intelligence & reports
Network Capture Input
PCAP Upload • Tap Stream
Feature Extraction
Metadata • SSL/TLS signals
BlackCrypt AI Engine
Behavioral threat detection for encrypted traffic
Results & Enrichment
Findings, intelligence & reports
Privacy by Design
• Metadata-focused analysis
• Payloads stay out of scope
• PCAPs removed after analysis
• Clear retention windows
TLS-Native Analysis
• Handshake patterns
• Version & cipher negotiation
• Flow dynamics
• Fingerprints & cert hints
AI-Powered Behavioral Detection
• Behavior-based AI detection
• Learns from encrypted-session patterns
• Hybrid decision flow
• Multi-model AI stack
Use Cases
Where BlackCrypt fits into incident response, threat hunting, lab evaluation and encrypted network visibility.
Incident response & triage
Suspicious traffic: Get a fast AI-powred behavioral triage signal on encrypted sessions so you can decide how to respond.
Retrospective analysis
Retrospective hunts: Review past captures to surface encrypted threat patterns your existing stack may have missed.
Lab & Evaluation
Lab testing: Evaluate encrypted-traffic detection on realistic traffic captures before you commit to a rollout.
Perimeter capture review
Internet-edge captures: Review north–south traffic captures for suspicious encrypted patterns at your perimeter.
Critical segments & remote access
High-value segments: Review VPN, DMZ and critical application traffic captures where payload inspection has limited TLS visibility.
Technology
BlackCrypt AI Engine
AI-Powered behavioral detection for encrypted-session metadata.
Machine learning models
SSL/TLS metadata
Confidence-aware classification
Threat Detection Model
Benign vs malicious encrypted sessions
Malware Family Detection
25+ families supported
View supported families
Advanced analysis can add family and threat-context labels when the encrypted-session evidence is strong enough.
High-impact frameworks, C2 and ransomware
WannaCry
Cobalt Strike
PoshC2
Merlin C2
Sliver / DonutLoader
GC2
Loaders, banking and modular malware
TrickBot
Emotet
QakBot
PikaBot
Dridex
IcedID
Latrodectus
Raspberry Robin
GootLoader
Bumblebee
Matanbuchus
BazaLoader
HTBot
Stealers, RATs and remote-access context
Lumma Stealer
Octopus
AsyncRAT
SectopRAT
Specula
Google RAT
Botnet, campaign and known-context labels
Neris
SmartApeSG
Numinon
Mansabo
Labels are shown only when confidence and policy checks allow. Low-confidence or unsupported matches are shown as Unclassified rather than forced into a named family.
Threat Category Detection
6 supported categories
View threat categories
Post-Exploitation
C2
Infostealer
Remote Access
Loader
Botnet / Spambot
Type context is used for triage and reporting. If confidence is insufficient, BlackCrypt keeps the malicious-session detection and marks the type as Unclassified.
Roadmap
What is available today, what is coming next, and where BlackCrypt research is heading.
Available now
Threat Analysis Platform
- Upload network captures for encrypted-traffic analysis
- Behavioral AI scoring for encrypted-session triage
- Advanced enrichment with malware classification, threat intelligence, and PDF reporting
Coming next
Encrypted-Traffic NDR
- Continuous detection and response for suspicious encrypted network behavior.
- AI detection and threat-intelligence enrichment beyond upload workflows
- SIEM/export integrations for operational handoff
Expansion
Research Lab
- Expand proprietary encrypted-traffic datasets and validation coverage
- Broaden malware coverage and improve classification precision
- Research agent workflows for report QA, investigation guidance, and detection-engineering support
Available now
Threat Analysis Platform
- Upload network captures for encrypted-traffic analysis
- Behavioral AI scoring for encrypted-session triage
- Advanced enrichment with malware classification, threat intelligence, and PDF reporting
Coming next
Encrypted-Traffic NDR
- Continuous detection and response for suspicious encrypted network behavior.
- AI detection and threat-intelligence enrichment beyond upload workflows
- SIEM/export integrations for operational handoff
Expansion
Research Lab
- Expand proprietary encrypted-traffic datasets and validation coverage
- Advance next-generation AI modelling, calibration, and evaluation workflows
- Broaden malware coverage and improve classification precision
Contact
For product evaluations, pilots, customer inquiries, and research partnerships, contact: contact@blackcrypt.ai
Customer support is handled directly from within the BlackCrypt portal.