Beyond the Data: Bayesian Cognitive Priors for Human-Centered OSINT Automation

DOI

10.2991/978-94-6239-610-4_32How to use a DOI?

Keywords

Open-Source Intelligence (OSINT); Bayesian Inference; Cognitive Priors; Human-Centered AI; Probabilistic Evidence Fusion; Decision-Theoretic Control; Human-in-the-Loop Intelligence; Intelligence Automation; Ethical AI and Governance

Abstract

Open-source intelligence (OSINT) pipelines can gather and connect huge amounts of publicly available data, but they still don’t have a formal way to show the probabilistic reasoning and intuitive heuristics that human analysts use when they look at evidence and get information. This paper presents Beyond the Data, a Bayesian cognitive framework that methodically represents human intuition as explicit probabilistic priors and supervisory signals in automated OSINT fusion and decision-making systems. The framework transforms structured analyst traces such as think-aloud protocols, interaction logs, and HUMINT tip annotations into parameterized cognitive priors for hierarchical Bayesian belief networks that collectively encapsulate source reliability, temporal dependencies, and culturally contextualized cues.

Beyond the Data integrates passive evidence fusion and active intelligence acquisition via a decision-theoretic control layer that maximizes expected information gain while limiting actions according to legal, ethical, and provenance-sensitive cost functions. The proposed framework shows better posterior calibration, fewer false positives, and analyst-aligned escalation behaviour than traditional fusion baselines when tested on three representative tasks: entity disambiguation, temporal event reconstruction, and deception detection. Quantitatively, our Bayesian-intuition models attain a maximum enhancement of 34% in evidence relevance ranking and a 28% decrease in misclassification error across diverse OSINT datasets. We finish with a talk about governance tools, such as auditable inference chains, rollback-safe updating, and ethical protections for using people in the loop in operational intelligence settings.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sairam Palabindela
AU  - Sai Madhuri Konnipati
PY  - 2026
DA  - 2026/05/05
TI  - Beyond the Data: Bayesian Cognitive Priors for Human-Centered OSINT Automation
BT  - Proceedings of the First International Conference on Advances in Forensics and Cyber Technologies (ICFACT 2025)
PB  - Atlantis Press
SP  - 373
EP  - 379
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-610-4_32
DO  - 10.2991/978-94-6239-610-4_32
ID  - Palabindela2026
ER  -