Intelligence Analysts Adopted Automated Data Fusion Tools

As global data volumes scale toward 700 zettabytes by 2030, analysts are turning to AI-driven fusion platforms.

Updated on Sept. 22, 2026 in Artificial Intelligence

Isometric editorial illustration of a cluster of server-grade metallic cubes and glass nodes representing data fusion architecture. — Intelligence analysts are increasingly implementing automated data fusion platforms to manage the surging volumes of global data while maintaining strict security compliance. AI Illustration. Upload story photo >

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Do you trust AI-driven automation to handle sensitive data analysis more reliably than humans?

Intelligence expert Brittany Mason has advocated for the integration of automated data fusion platforms like Fivecast ONYX to handle the surging volume of global data. The shift marks a move toward using AI to triage intelligence collection in response to the growing operational risks of manual processing.

Why it matters

Managing massive, disparate datasets manually creates significant bottlenecks and increases non-compliance risks under strict security frameworks. Automation enables human analysts to focus on higher-level pattern recognition while maintaining adherence to federal data standards.

Fivecast ONYX uses AI to aggregate data discovery and collection into a single system that aligns with the NIST Special Publication 800-171 security controls. The platform processes large-scale information to identify relationships, routing findings to human analysts for verification.

The players

Brittany Mason

An intelligence professional advocating for the modernization of analysis workflows through automated data fusion.

Fivecast

A developer of AI-driven data collection and discovery software including the ONYX, MATRIX, and LUNEX platforms.

Potomac Officers Club

A professional organization that facilitates summits and discussions among government and defense-focused technology leaders.

The details

Automated data fusion platforms utilize machine learning algorithms to triage and synthesize high-velocity data streams that would otherwise overwhelm manual workflows. By processing raw inputs into consolidated patterns, the technology allows human analysts to review specific relationships and provide feedback that continuously refines the system's performance. These platforms, which include Fivecast's ONYX, MATRIX, and LUNEX modules, are designed to ensure that intelligence workflows maintain compliance with the National Institute of Standards and Technology (NIST) 800-171 standard, a set of security requirements for protecting sensitive information in non-federal systems.

September 24, 2026: The Potomac Officers Club hosts the 2026 Intel Summit.
2026: Global data generation is projected to exceed 230 zettabytes.
2030: Global data generation is expected to surpass 700 zettabytes.

The Tech Race

The adoption of these platforms follows a broader trend toward hardening intelligence infrastructure against the security risks of manual processing. Industry players are now prioritizing systems that meet the rigorous NIST 800-171 standard to ensure data integrity as collection volumes scale.

This transition impacts government and private sector analysts by shifting their primary workflow from manual data gathering to the oversight of AI-filtered intelligence insights. Organizations will need to ensure their software stacks align with NIST 800-171 standards to remain compliant with federal security mandates.

The takeaway

The sheer scale of global data generation is rendering manual intelligence analysis obsolete, making automated fusion tools a structural necessity. Watch for compliance updates to NIST 800-171 as more agencies transition to automated platforms over the next four years.