Securing Intelligence: Open-Source AI Across the Lifecycle

Origins of AI-Driven Log Analysis Leveraging artificial intelligence (AI) for security log analysis and detection was once a cutting-edge concept emerging from academic research. In the late 1980s, researchers introduced the first anomaly-based intrusion detection systems (IDS) to automatically flag suspicious behavior – a novel shift away from purely manual or signature-based monitoring  (Barton & Li, 2019). For example, 1987 marks a seminal point when an IDS model was proposed to profile “normal” activities and alert on deviations, laying the groundwork for machine learning in cybersecurity. By the late 1990s, academic and government initiatives (notably DARPA’s 1998–99 evaluations) provided benchmark datasets to advance ML-based intrusion detection (Barton & Li, 2019). However, these early AI-driven solutions remained largely experimental; few were practical for real-world use, given the era’s computational limits and limited training data (Barton & Li, 2019). This f...

Abstract Artificial intelligence (AI) systems are becoming integral to enterprise operations, yet they expose organizations to novel security threats—especially when open-source models are adopted without rigorous vetting. This paper presents a sociotechnical framework that integrates technical defenses with governance, aligning NIST’s Cybersecurity Framework (CSF) and AI Risk-Management Framework (AI RMF) with ISO/IEC 27001 controls and Zero-Trust principles. Drawing on recent industry surveys, vendor tool analyses, and documented incidents of backdoored models, the framework prescribes multilayered safeguards across the AI lifecycle: secure development and supply-chain validation, adversarially robust deployment, continuous monitoring and incident response, and human-centered policies and training. The result is a defense-in-depth strategy that enables enterprises to leverage AI confidently while mitigating risks such as data poisoning, prompt injection, model theft, and the leakage ...