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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...
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Securing AI Models in Enterprise: A Sociotechnical Framework

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 ...
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Exploiting the Model Context Protocol: Deep Dive into the GitHub MCP Vulnerability

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Introduction In May 2025, security researchers at Invariant Labs disclosed a critical vulnerability in the Model Context Protocol (MCP) integration for GitHub. MCP is a new open protocol that connects large language model (LLM) agents with external tools and data sources in a standardized way. The affected GitHub MCP server (an open-source integration with ~14k stars) enables AI agents to interface with GitHub APIs for tasks like reading repository content, managing issues, and automating workflows. Invariant’s findings show how an attacker can abuse this integration via a prompt injection in a public GitHub issue to hijack an AI agent and leak data from private repositories. This deep dive will examine the MCP architecture, explain how the vulnerability arises, and analyze the real-world risks—ranging from compromised GitHub Actions to supply chain integrity issues—before discussing mitigation strategies for secure MCP use in AI pipelines. MCP Architecture and How It Works What is MCP...
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AI-Driven Cybersecurity Innovation Integration Plan

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AI-Driven Cybersecurity Innovation Integration Plan Introduction This document outlines a comprehensive sociotechnical plan to integrate cutting-edge AI-driven cybersecurity innovation into the organization's defenses. The innovation in focus is a predictive threat intelligence and autonomous incident response system powered by generative AI. In cybersecurity, this emerging technology analyzes vast threat data and anticipates attacks, then acts to contain them with minimal human intervention. Such AI-driven solutions are increasingly seen as transformative, shifting security from reactive to proactive. They leverage AI's speed and pattern-recognition capabilities – for example, AI can rapidly analyze large datasets and detect complex attack patterns, making it an invaluable tool for identifying and mitigating threats in today's fast-evolving landscape Kamran (2025). This plan describes the scope, purpose, driving forces, challenges, and recommended method for implementing t...
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