Isolate knowledge
Organize authorized reports, notes, evidence, and indicators in separate Knolo packs scoped to an organization and case.
● Cybercrime case intelligence
Find the patterns hiding across cybercrime reports.
Put cybercrime reports and evidence into the system. CCRNet helps investigators find what repeats, what connects, and what deserves closer review—while building a responsible foundation for cybercrime AI research, training, development, and prediction.
Your organization gets immediate value from its own private data. No public database required.
CCRNet is in active research and development
The architecture, models, and interfaces described here are being researched and prototyped. We do not currently have an expected launch date.
FROM INTAKE TO INSIGHT
Start with the cybercrime data you already have.
CCRNet will use Knolo to build isolated knowledge packs for each organization and case, turning private reports and evidence into searchable intelligence without making a shared network a prerequisite.
Search deterministically
Run reproducible cross-report searches and trace results to the supporting cybercrime source material.
Review connections
Surface evidence-backed connection suggestions for human review before they influence an investigation, model, or prediction.
RESEARCH & DEVELOPMENT
Research is the work—not a footnote.
CCRNet began as a research platform for studying how cybercrime reports can be structured, categorized, and analyzed, and how carefully prepared report data can support artificial-intelligence and blockchain-based systems.
Our current R&D examines isolated knowledge architecture, deterministic retrieval, cross-report pattern analysis, human-reviewed connection suggestions, cybercrime model training and evaluation, and responsible approaches to prediction.
Explore our research history and publicationsCurrent R&D tracks
- 01Knowledge infrastructureIsolated Knolo packs scoped to organizations and individual cases.
- 02Retrieval & connectionsReproducible search with traceable sources and human-reviewed suggestions.
- 03Cybercrime AIDataset development, training, evaluation, and evidence-aware prediction research.
- 04Responsible exchangeOpt-in methods for sanitizing and matching selected intelligence indicators.
CCRNET INTELLIGENCE EXCHANGE
Private first. More powerful together.
Every customer begins by connecting its own case data. When ready, members can opt in to a carefully governed exchange that expands the field of view without making participation a prerequisite.
“Contribute selected, sanitized indicators to the CCRNet Intelligence Exchange and receive matches against intelligence shared by other members.”
Participation will be selective, transparent, and reversible. Raw reports and evidence stay outside the exchange; contributors choose which eligible indicators to share.
Designed for responsible intelligence
- ✓Private by defaultYour organization’s workspace and source material remain isolated.
- ✓Explicitly opt inNothing enters the exchange simply because it is in CCRNet.
- ✓Sanitized indicatorsShare selected signals, not complete reports or case files.
- ✓Human verificationMatches are investigative leads, never findings of wrongdoing.
CYBERCRIME AI, RESPONSIBLY
Intelligence for research, training, and prediction.
Purpose-built data
Develop cybercrime-specific datasets and models around reports, tactics, infrastructure, indicators, and outcomes.
Evidence-backed development
Evaluate training and prediction work against traceable source material and deterministic retrieval results.
Controlled access
Organization and case isolation, permissions, retention controls, and auditable activity are foundational requirements.
Human-reviewed outputs
AI predictions and connection suggestions are leads—not verdicts—and must receive qualified human review.
ONE NETWORK · FOUR CONNECTED LAYERS
From fragmented signals to an organization-wide intelligence program.
CCRNet is designed as a connected system: reports and responsibly collected threat intelligence become governed knowledge, investigators turn that knowledge into leads, and approved datasets power carefully evaluated fraud and cybercrime models.
Explore the platform previewSubmit structured intelligence
Capture incidents, financial loss, entities, indicators, evidence references, and consent in one guided flow.
Individuals · Analysts · Member organizationsMap external threat activity
Organize authorized dark-web, Telegram, marketplace, forum, and illicit-channel intelligence with provenance and handling controls.
Threat researchers · Intelligence teamsConnect cases with Knolo
Search authorized case packs, review explainable connections, map recurring infrastructure, and preserve source lineage.
Investigation teams · Fraud operationsBuild evidence-aware models
Access governed datasets, evaluation workspaces, and APIs for fraud scoring, trend forecasting, and model training.
Enterprise data teams · ResearchersR&D PREVIEW
See the direction of our technical work.
The planned CCRNet API is part of ongoing research into report ingestion, deterministic search, connection review, and LLM-assisted cybercrime workflows.
Read the technical R&D documentationCCRNet and its API are not generally available. There is no expected launch date, and the documented architecture and interfaces may change as research progresses.