Orion Constellation ::: Cognitive Synthesis

An implementation of Gretel AI’s Synthetic Data Generator Trained on Cyber Threat Signals.

The conceptual and exciting integration of Gretel AI for generating synthetic adversarial traffic on Google Cloud Platform (GCP) underscores a offensive-defensive cybersecurity strategy. When combined with the entire Orion Constellation and Tool belt, will provide a connected, self reinforcing and proactive threat intelligence platform. This approach, leveraging in machine learning (ML), ML Ops, synthetic data, and immutable ledgers, promises a holistic solution to counteract the escalating cyber threat landscape. Composed of “Orion Hunts” – “Orion Agents” – “Cognitive Synthesis” as well as reporting and threat intelligence data connectors for intelligent processing of signals and signatures of Adversarial models.

Constellation Summary:

Easily accessible Open Source Machine Learning technology brings with it an augmented threat of cyberattacks, where threat actors can automate many of their attacks and the systems and monitoring we have used in the past are less useful The “Orion Constellation” project embodies a strategic initiative aimed at leveraging various technology to fortify cybersecurity defenses. This initiative spans several key components:

Orion Hunts: A platform focused on simulating and tracking adversarial cyber activities. Collecting the fingerprints and tactics, techniques and procedures of threat actors identified as a collective distributed network of intelligence gathering.
Orion Agents: Autonomous entities powered by Microsoft Autogen to detect and counteract cyber threats based on our threat intelligence aggregator. Threat triage and distribution with a human in the loop.
Cognitive Synthesis: Utilizes transformer models to generate synthetic data for training AI systems, enhancing their capability to predict and mitigate cyberattacks. Synthetic data is improving in quality and the data exhaust generated by the Orion Constellation our research will be focused on how to best use existing heuristics for analogous or potential threat signatures.

Currently: Synthetic Data Generation

Synthetic data generation, spearheaded by initiatives like Gretel AI and MostlyAI, plays a pivotal role in training robust ML models. These models are designed to recognize and adapt to evolving cyber threat patterns, thereby bolstering defensive mechanisms against such adversarial tactics. The generation of synthetic adversarial traffic allows for a comprehensive and dynamic threat modeling, crucial for preemptive defense strategies.

Implementation on GCP with Gretel AI

The deployment of Gretel AI on GCP facilitates a scalable and efficient platform for synthetic data generation and processing. Key components of the GCP implementation include:

Cloud Compute Services: For scalable processing power to train complex ML models and generate synthetic data.
ML Operations & Continuous Learning: Between synthetic data generation, improving the agents and hunter through positive feedback between each component, coordinated MLOps and quick digestion of threat intelligence will make this component significant.
BigQuery and Cloud Storage: For storing and managing vast datasets and synthetic data outputs.
AI Platform and ML Ops Tools: For streamlined model training, versioning, and deployment, ensuring that the cybersecurity models are always at the cutting edge.
Data Preprocessing and Transformation: Utilizing GCP’s data engineering tools to format and prepare datasets for optimal training efficiency within the Gretel framework.

Immutable Ledgers for Traceability

The use of blockchain or immutable ledgers within this ecosystem ensures traceability and accountability, critical aspects of a reliable cybersecurity framework. These technologies facilitate a transparent and tamper-proof record of cyber events, AI decisions, and data provenance, essential for auditing and continuous improvement of the defense mechanisms.

The Cognitive Synthesis Project

The Cognitive Synthesis project within the Orion Constellation framework exemplifies the practical application of these technologies. It involves:

Dataset Utilization: Leveraging both real and synthetic datasets to train AI models capable of understanding and countering cyber threats.
Data Preprocessing: Implementing sophisticated data preprocessing techniques to optimize datasets for AI training, enhancing model accuracy and efficiency.

The amalgamation of GCP’s robust cloud infrastructure with fun technologies like Gretel AI for synthetic data generation and immutable ledgers represents a formidable strategy in the cyber defense arsenal. This innovative approach not only anticipates and mitigates cyber threats but also fosters a dynamic and continuously evolving cybersecurity posture. The “Orion Constellation” project, particularly through initiatives like “Cognitive Synthesis,” heralds a new era of cyber defense, leveraging the full spectrum of AI and machine learning capabilities to secure digital assets and infrastructures against the ever-evolving landscape of cyber threats.

The Orion Constellation is one of the projects within the Synavate Labs experimental research & product development group.

Github Repos:

Orion-Constellation here

Orionthunts-ai here

Threat Intelligence Dataset here