The Data –> Intelligence Pipeline
Morpheus Goals::
Automate the collection, collation, and processing of data, and the delivery of intelligence. Recognize the distinction between raw data and contextually relevant information. Grow to become a recognized source of intelligence. Mitigate biases, whether cultural, temporal, or geographical. Use the developed intelligence as data to train defensive models in different verticals.
DataXj
Raw, unprocessed facts collected from diverse sources. Data points are discrete and lack context, making them uninformative in isolation. Examples include IP addresses, timestamps, URLs, and unstructured text logs. The primary goal is to collect as much relevant data as possible to feed into the processing pipeline.
VS
IntelligenceYk
Processed and analyzed data that has been contextualized and interpreted to provide actionable insights. Intelligence is derived from data through techniques like machine learning, natural language processing, and semantic analysis. It is the refined output that supports decision-making and strategic planning. Intelligence transforms raw data into meaningful information, such as identifying an APT group’s activities, detecting patterns in network traffic, or predicting potential security threats.
Context::
Embed data within its relevant context to enhance understanding and decision-making. Utilize data structures such as graphs with additional semantic knowledge embedded in them (Knowledge Graphs). Identify connections between the MITRE ATT&CK and MITRE 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 databases to store relatively static, unchanging information in a scalable and flexible manner. Vector databases for tasks where similarity and semantics are paramount and operate in near real-time. As the Orion network matures, it will contribute high-quality intelligence reports. Every node in the network will provide data to the platform for analysis and reporting. Integrate feedback from node operators to continuously improve the intelligence pipeline using reinforcement learning.
SummaryLOl::
D2Intel aspires to semi-automate the upper end, the hoover 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, acknowledging the complexity and the need for continued innovation.
Snyata
Aylex Riom 