D2Intelligence: The Data --> Intelligence Pipeline

Goals:

1. Automate the collection, collation and processing of data, processing --> and delivery of intelligence.
2. Recognize the distinction between raw data and contextually relevant information.
3. Grow to become a recognized source of intelligence.
4. Mitigate bias whether cultural, temporal or geographical.
5. Use the developed intelligence as data to train defensive models in different verticals.

Data:

Collect raw data from diverse sources.

Intelligence:

Transform raw data into actionable intelligence using Traditional ML and Contemporary LLM technology as well as semantic knowledge sources

Context:

1. Embed data within its relevant context to enhance understanding and decision-making.
2. Use data structures such as graphs with additional semantic knowledge embedded in them (Knowledge Graphs)
3. Find the connections between the M&TRE Attack and Atlas Frameworks

Outliers:

• Identify and analyze deviations from the norm to uncover potential threats.
• Classify new threats and contribute to the pool of Data.

Data Sources

• Traditional Vendor Threat Feeds wherever feasible.
• NoSQL Database: store relatively static, unchanging information in a scalable and flexible NoSQL database.
• Vector Databases: leverage vector databases for tasks where similarity and semantics are paramount and at close to real time

Orion Network:

• As the Orion network matures, it will contribute high-quality intelligence reports.
• Every Node in the network will be providing Data to the Platform for Analysis and Reporting

Reinforcement Learning:

• Integrate feedback from node operators to continuously improve the intelligence pipeline.

Summary

D2Intelligence automates the upper echelons of the intelligence pipeline, from data ingestion to the production of summative reports and alerting systems. A human remains in the loop when confidence levels are low, complemented by random sampling to ensure reliability. The system aims to achieve a dependable false positive/negative rate, leveraging novel technologies and methodologies.

The feasibility of this ambition hinges on the trajectory of our technological advancements. For now, we plead insanity as always.