Description
Objective: The Defense Logistics Agency (DLA) manages a vast and complex global supply chain, underpinned by a sprawling digital infrastructure. The objective is to address the fact that traditional cybersecurity approaches, which rely heavily on manual processes and human expertise, are increasingly strained and struggle to scale effectively against an evolving threat landscape. This landscape is characterized by expanding attack surfaces, a persistent shortage of skilled cybersecurity personnel, and the rise of AI-enabled adversaries. This effort seeks to introduce a new paradigm to automate and scale DLA's cyber defense and assessment capabilities, mitigating significant risks to the agency's critical logistics and supply chain data. Description: DLA seeks SBIR project opportunities for an agentic AI framework designed to strengthen its defensive cybersecurity posture and automate penetration testing. The proposed solution involves a team of specialized AI Agents, each configured with specific tools, knowledge, and roles, that collaborate to execute complex cybersecurity workflows. The core innovation lies in a collaborative, multi-agent framework that mimics the workflow of a human cybersecurity team, enabling autonomous execution of complex, multi-step tasks. Specific agent roles and functions of interest include: Project Management: Devising high-level plans for security tasks, such as network enumeration or vulnerability assessment, using algorithms and security frameworks (e.g., MITRE ATT&CK). Cyber Analysis: Interpreting raw data from scans and tests to identify and prioritize defensive actions and vulnerabilities, utilizing vulnerability databases and threat intelligence feeds. Code Generation & Execution: Translating high-level plans and priorities into executable code and command-line instructions (e.g., NMAP, Metasploit) and running them in emulated environments. Vulnerability Research: Conducting deep-dive analysis on specific vulnerabilities using Retrieval-Augmented Generation (RAG) against a corpus of CVEs, CPEs, and technical documentation. Research and Development (R&D) efforts selected under this topic shall demonstrate and involve a degree of risk where the technical feasibility of the proposed work has not been fully established. Keywords: Agentic AI, Cybersecurity, Automation, Penetration Testing, Network Security, Vulnerability Management, Artificial Intelligence, MITRE ATT&CK CMMC Level: Level 2 (Self)