Automating the Intelligence Cycle

In the high-stakes environment of U.S. Government intelligence operations, efficiently managing and utilizing vast amounts of data is crucial for maintaining national security. By integrating Retrieval Augmented Generation (RAG) with semantic knowledge graphs, agencies such as the CIA, DIA, DHS, and SOCOM can significantly enhance their intelligence tradecraft. This article explores how Aktiver’s advanced automation workflows and security features, including Role-Based Access Control (RBAC) using DoD-approved Keycloak, enable these agencies to build, test, and deploy AI Agents with human-level reasoning at scale, while ensuring secure, role-specific access to sensitive intelligence data and AI models.

The Strategic Importance of Aktiver in Intelligence Operations

In the rapidly evolving intelligence landscape, agencies like the CIA, DIA, DHS, and SOCOM are tasked with gathering, analyzing, and acting on vast amounts of data in real-time. Aktiver provides a comprehensive platform that automates the transformation of raw data into structured semantic knowledge graphs. These graphs serve as the backbone for advanced reasoning tasks before the data is processed by Large Language Models (LLMs).

Leveraging Aktiver for Secure and Scalable AI Frameworks

Aktiver's platform automates the conversion of disparate and unstructured data from sources such as GEOINT, OSINT, SOCMINT, or other streams into comprehensive semantic knowledge graphs. This process begins with data ingestion, where users can upload data in a wide variety of formats—whether it's CSV files, JSON, SQL dumps, PDFs, or drone and satellite imagery—giving agencies the flexibility to input data from multiple sources without manual formatting. Once uploaded, Aktiver’s automation builds a semantic "phylum" of data, classifying and linking data points to embed human-like reasoning between them. This allows intelligence analysts to work with context-rich data for more reliable decision-making.

Data Ingestion, Management, and Access Control

Data ingestion is followed by automated classification and transformation into a preliminary taxonomy. Aktiver then creates semantic knowledge graphs that capture relationships between entities, such as people, places, and events. This is critical for intelligence agencies, allowing them to quickly process and analyze diverse data across multiple domains to support mission-critical decisions. For enhanced security, Aktiver integrates with Keycloak to enable Role-Based Access Control (RBAC). This ensures that only authorized personnel have access to sensitive data and models, with access restricted by roles tailored to data classification levels, such as Top Secret or TS-SCI.

Automated Semantic Knowledge Graph Creation

Aktiver automatically converts classified data into a semantic knowledge graph, enabling intelligence analysts to export the data as ontologies specific to various intelligence or operational domains. These ontologies standardize and structure data, ensuring seamless integration into AI models optimized for intelligence use cases. Additionally, the data is loaded into an automated linked data environment that supports advanced reasoning and analysis.

Spatial Inference and Reasoning

Before the data is processed by LLMs, Aktiver applies spatial inference and reasoning tasks to enhance the understanding of spatial relationships, particularly useful in GEOINT operations. By applying these reasoning capabilities, Aktiver enables more accurate and contextually rich outputs from AI models, improving the quality of intelligence insights.

Aktiver’s “One-Click” Automation for Custom Intelligence Models

Aktiver empowers U.S. government agencies with one-click automation to create and deploy custom AI models tailored to their specific needs. By combining open-source datasets with pre-trained models, users can fine-tune models to optimize performance for unique intelligence use cases, such as GEOINT or SOCMINT.

How it works

1. Upload Your Data: Aktiver accepts a wide variety of data formats—whether it’s GEOINT data (e.g., satellite imagery), OSINT (publicly available data), SOCMINT (social media intelligence), or unstructured text from intelligence reports. This flexibility allows intelligence agencies to input data from multiple sources, such as intercepted communications or SQL file dumps, without the need for manual data formatting.
2. Automated Data Phylum Creation: After uploading, Aktiver automates the creation of a semantic "phylum" of intelligence data, classifying and linking data points from disparate sources. This capability is crucial for intelligence agencies, as it allows them to embed human-like reasoning into relationships between data points—whether it's linking individuals to locations or events, or detecting patterns across intelligence streams. This provides a foundation for more nuanced and intelligent decision-making in operations.
3. Export Ontologies: Once data is processed, users can export the intelligence data as ontologies specific to various intelligence domains, such as SIGINT (signals intelligence), HUMINT (human intelligence), or GEOINT. These ontologies standardize the data, making it interoperable across various AI models and intelligence platforms, ensuring that the data is ready for further analysis or model training.
4. Automated Linked Data Environment: The processed intelligence data is loaded into ontologically driven data structures, creating a linked data environment that allows for the seamless integration of complex relationships between entities, such as suspects, targets, and geographic locations. This environment ensures that AI models can operate with logically organized data, making it easier to identify hidden patterns or threats in intelligence operations.
5. Drag-and-Drop Interface for AI Agent Design: Aktiver offers a drag-and-drop interface that enables intelligence analysts to design AI agents capable of making human-like decisions and automating tasks in the intelligence workflow. These AI agents can be programmed to analyze satellite imagery, monitor social media for emerging threats, or automate report generation, enhancing the overall efficiency of intelligence operations.

Summary & Contact

Aktiver's automation enables agencies such as the CIA, DIA, and SOCOM to build and deploy AI frameworks at scale, enhancing their ability to manage intelligence data securely and effectively. By leveraging Aktiver, the intelligence community can stay ahead of emerging threats, driving innovation and operational excellence across national security operations. Contact us at aktiver.io to learn more.

Academic References

1. NATO STO. (2022). Automation in the Intelligence Cycle: Defense Intelligence Enterprise Knowledge Graph – An Evolving, Logic-Governed, Computable Graph. Knowledge Representation and Reasoning: A Review of the State of the Art and Future Opportunities. NATO STO Technical Report (STO-TR-IST-ET-111). Retrieved from https://www.sto.nato.int/publications/STO Technical Reports/STO-TR-IST-ET-111/$$TR-IST-ET-111-ALL.pdf

   
   

2. Schramm, S., Wehner, C., & Schmid, U. (2023). Comprehensible Artificial Intelligence on Knowledge Graphs: A Survey. arXiv preprint arXiv:2404.03499. Retrieved from https://arxiv.org/pdf/2404.03499

   
   

3. Hello, N., Di Lorenzo, P., & Calvanese Strinati, E. (2024). Semantic Communication Enhanced by Knowledge Graph Representation Learning. arXiv preprint arXiv:2407.19338. Retrieved from https://arxiv.org/pdf/2407.19338

   
   

4. Paparidis, E., & Kotis, K. (2021). Knowledge Graphs and Machine Learning in Biased C4I Applications. arXiv preprint arXiv:2106.09258. Retrieved from https://arxiv.org/pdf/2106.09258

   
   

5. Ekaputra, F. J., Llugiqi, M., Sabou, M., Ekelhart, A., Paulheim, H., Breit, A., ... & Auer, S. (2023). Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG. Retrieved from https://www.researchgate.net/profile/Soeren-Auer/publication/370935951_Describing_and_Organizing_Semantic_Web_and_Machine_Learning_Systems_in_the_SWeMLS-KG/links/6482e3402cad460a1bff2ec2/Describing-and-Organizing-Semantic-Web-and-Machine-Learning-Systems-in-the-SWeMLS-KG.pdf

   
   

6. Delgrande, J. P., Glimm, B., Meyer, T., Truszczynski, M., & Wolter, F. (2023). Current and Future Challenges in Knowledge Representation and Reasoning. arXiv preprint arXiv:2308.04161. Retrieved from https://arxiv.org/pdf/2308.04161