How Automation Improves AI Security Assessments

NVIDIA's Breakthrough in CVE Analysis - The complexity of modern software dependencies has made vulnerability management a monumental challenge—with over 200,000 CVEs reported by 2024, traditional patching approaches are no longer sufficient. NVIDIA’s Agent Morpheus, a generative AI-powered solution that transforms how enterprises analyze and mitigate security risks. 🔍 Why This Matters Agent Morpheus doesn’t just scan for vulnerabilities—it determines exploitability by contextualizing threats within your software environment. For example, it can identify whether a vulnerable .jar file is even executable or if a CVE is a false positive. This precision slashes unnecessary patching and keeps critical software delivery on track. ⚡ Key Innovations - AI Agents & RAG: Combines retrieval-augmented generation with fine-tuned Llama3 models to automate threat analysis, generating actionable checklists and VEX-compliant justifications. - 9.3x Faster Triage: Parallel processing via Morpheus framework reduces analysis from hours to seconds—20 CVEs in 5 minutes vs. 47 minutes! - Continuous Learning: Human analyst feedback fine-tunes the system, creating a self-improving loop for accuracy. 🌐 Enterprise-Ready Integration Seamlessly embedded into CI/CD pipelines, Agent Morpheus triggers on container uploads, scans with tools like Anchore, and delivers decision-ready insights to security dashboards. It handles thousands of concurrent requests, proving scalability isn’t just a buzzword. 👏 Kudos to NVIDIA for pushing the boundaries of AI in cybersecurity! This isn’t just about faster scans—it’s about smarter, risk-aware decisions that keep enterprises secure without sacrificing agility. 🔗 Dive deeper into how generative AI is redefining vulnerability management: https://lnkd.in/gYjRRkUm

🤖 𝐔𝐬𝐢𝐧𝐠 𝐀𝐈 𝐭𝐨 𝐡𝐚𝐧𝐝𝐥𝐞 𝐂𝐕𝐄𝐬 𝐚𝐭 𝐬𝐜𝐚𝐥𝐞 NVIDIA: Agent Morpheus: CVE Analysis at Enterprise Scale Databricks: VulnWatch: AI-Enhanced Prioritization of Vulnerabilities 1️⃣ Applying Generative AI for CVE Analysis at an Enterprise Scale This NVIDIA post describes an AI-powered workflow called "Agent Morpheus" that automates CVE analysis and exploitability assessment. The system uses RAG (multiple vulnerability databases and threat intelligence sources, the project’s source code, SBOM, docs, Internet search) with four fine-tuned Llama3 LLMs, AI agents, and tools to autonomously investigate CVEs, determine exploitability, and generate VEX documents. Agent Morpheus integrates with container registries and security tools to automate the process from container upload to VEX document creation. By Bartley Richardson, Nicola Sessions, Michael Demoret, Rachel Kay Allen, Hsin Chen. 📎 https://lnkd.in/gq5S_pqR ---- 2️⃣ VulnWatch: AI-Enhanced Prioritization of Vulnerabilities Anirudh Kondaveeti describes Databricks' AI-driven system for detecting, classifying, and prioritizing vulnerabilities, achieving 85% accuracy in identifying business-critical issues, and “no false negatives in back-tested data.” 🤯 The system ingests CVE data from multiple sources, extracts relevant features (CVSS, EPSS, availability of exploit or patch, …), and uses an ensemble of scores (severity, component, topic) to prioritize vulnerabilities. It leverages LLMs and vector similarity to match the identified library with existing Databricks libraries, and employs automated instruction optimization to improve accuracy. This approach has reduced manual workload by 95%, allowing the security team to focus on the most critical 5% of vulnerabilities. 📎 https://lnkd.in/gUrSk8-z #cybersecurity #ai

The promise of AI agents isn't about futuristic general intelligence - it's about practical automation of the mechanical aspects of security workflows: 1. Automating multi-step queries across different data sources 2. Pre-enriching alerts with relevant context before human review 3. Maintaining investigation state across analyst handoffs 4. Applying consistent triage methodologies regardless of alert volume These capabilities leverage existing SIEM foundations through APIs - your search systems, enrichment services, rules engines, data normalization, and alert history. No magic, just pragmatic integration with the tools you already use. For alert triage, this means transforming a linear checklist into a dynamic process. For investigation, it means eliminating the "context switching tax" that slows down even experienced analysts. The most valuable security tools don't replace human judgment - they amplify it by removing the friction that prevents that judgment from being applied efficiently. What security workflows are consuming too much of your team's time that could benefit from this new type of automation? #SIEM #SecurityAutomation #SOCEfficiency #SecurityEngineering

Great conversation with Kumar Saurabh on AI in SOC. Kumar Saurabh is the OG having built SIEM twice (ArcSight and SumoLogic) and SOAR (LogicHub) Video link in comments: The State of SOCs and the Talent Gap Kumar highlights the cybersecurity talent shortage, emphasizing the need for high-quality personnel rather than just increasing headcount. Traditional SOCs are structured into Tier 1 (entry-level), Tier 2, and Tier 3, with expertise increasing at each level. The challenge lies in handling the growing volume and complexity of security alerts while maintaining cost efficiency. AI Analyst for Tier 1 Automation Kumar argues that AI can fully replace human analysts in Tier 1 roles, citing successful deployments in production. He references a blind A/B test where AI outperformed human analysts in quality, speed, and cost. While Tier 2 and Tier 3 still require human oversight, AI significantly reduces their workload, allowing experts to focus on complex cases. Shifting SOC Structure AirMDR has adopted an AI-first SOC structure, where AI handles all Tier 1 tasks, Tier 2 analysts supervise AI, and Tier 3 experts refine the AI’s performance. This feedback loop ensures continuous improvement. The AI-Driven Alert Analysis Process Kumar outlines a three-stage process for AI-driven alert handling: Enrichment – Collecting contextual data (IP ownership, user roles, etc.). Decision-Making – Assessing whether an event is malicious, benign, or suspicious. Response – Taking automated or recommended actions. LLMs: System One vs. System Two Thinking Kumar differentiates between "System One" (fast, intuitive thinking) and "System Two" (deep, analytical reasoning). LLMs excel at System One tasks, making them suitable for structured decision-making but less effective at complex investigations. He advocates for a hybrid approach, combining LLMs with human expertise for higher-level reasoning. Dynamic Playbook Generation vs. SOAR Limitations Traditional SOAR platforms rely on rigid, pre-defined playbooks that lack adaptability. Kumar argues that LLMs enable dynamic, real-time playbook generation, making AI-driven SOCs more flexible and cost-effective. Threat Intelligence & Data Enrichment LLMs assist in analyzing threat intelligence reports and extracting useful insights. The key is formulating the right questions to maximize their reasoning capabilities. Data Placement & Edge Analytics Kumar suggests a hybrid analytics approach: simple filtering should be handled at the edge, while complex analysis (e.g., User and Entity Behavior Analytics) requires centralized processing. The Future of SOCs: Leaner & AI-Driven Kumar predicts that within 3-5 years, AI will dominate Tier 1 SOC operations, drastically reducing costs and improving efficiency. SOCs will become leaner, with human analysts focusing on AI supervision and complex security incidents.

I’ve seen the evolution of security operations firsthand. From manual alert triage to partially automated workflows, we’ve made progress—but it’s still not enough. The volume of threats is overwhelming, and traditional SOC models can’t keep up. Enter SOC 3.0. This AI-powered approach not only assists analysts but also enhances and speeds up their decision-making, transitioning security operations from reactive to proactive. How SOC 3.0 Changes the Game: - AI-Driven Triage & Remediation – Automatically classify, prioritize, and resolve alerts at scale. - Adaptive Detection & Correlation – AI continuously learns, reducing false positives and spotting novel threats. - Automated Threat Investigations – AI surfaces key insights instantly, cutting investigation time from hours to minutes. - Optimized Data Processing – Query data where it resides, eliminating unnecessary storage costs and vendor lock-in. The bottom line? SOC 3.0 empowers human analysts, reduces burnout, and ensures faster, more accurate threat response. Are you ready to embrace AI in your SOC? Let’s discuss. 🔗 Read more on the evolution of SOC and how AI is transforming security: https://lnkd.in/e2j2ZUUt #Cybersecurity #SOC #AI #ThreatDetection #SecurityOperations