Listen to this Post
The world of AI in cybersecurity is rapidly evolving, with new models, protocols, and agents transforming how security operations (SecOps) function. Below are key resources and insights into the latest advancements:
🧠 MCP: Building Your SecOps AI Ecosystem
Jack Naglieri explains how Model Context Protocol (MCP) is revolutionizing SecOps by enabling structured AI interactions.
🔗 https://lnkd.in/eg7nTr25
🤖 Why SOCs are Turning to AI Agents
Filip Stojkovski & Prophet Security break down how AI agents enhance security operations.
🔗 https://lnkd.in/egNQGB4g
⚙️ Security Operations with RunReveal’s MCP Server
Evan J Johnson & RunReveal demonstrate real-world MCP applications.
🔗 https://lnkd.in/eNAi2rbd
🌐 Google Sec-Gemini v1
Google’s new cybersecurity model designed to outperform existing AI in threat detection.
🔗 https://lnkd.in/eUyNeaaR
Floki: Building an AI Agentic Workflow Engine with Dapr⚡️
Roberto Rodriguez dives into multi-agent collaboration frameworks.
🕵️ Perplexity for the Darkweb
Thomas Roccia explores LLM-powered darkweb investigations.
🧩 Explainability in Security Products
Harry Wetherald discusses how LLMs solve the “black box” problem.
🔗 https://lnkd.in/efcvSDUg
🛡️ Microsoft Releases 6 New Security Agents
Latest AI-driven security tools from Microsoft.
🎣 AI-Powered Phishing Outperforms Elite Red Teams
AI agents now surpass human red teams in phishing simulations.
🔗 https://lnkd.in/ehuXP4UE
⚔️ Accelerated Threat Hunting with Open Weight LLM Models
Splunk’s approach to AI-powered threat detection.
👊 Rule-ATT&CK Mapper (RAM): Mapping SIEM Rules to TTPs Using LLMs
Automating threat detection with AI.
🎙️ AI for Security Podcast
Filip Stojkovski, Anshuman Bhartiya, Harry Wetherald discuss AI agents & LLM challenges.
🔗 https://lnkd.in/eQyrUBYV
You Should Know: Practical AI Security Commands & Tools
1. Running AI-Powered Threat Detection with Python
import tensorflow as tf
from transformers import pipeline
Load a cybersecurity-trained LLM
cyber_llm = pipeline("text-classification", model="google/sec-gemini")
threat_report = cyber_llm("Detect malicious activity in logs: [log data]")
print(threat_report)
2. Automating SIEM Rule Mapping with RAM
Clone Rule-ATT&CK Mapper git clone https://github.com/rule-attack-mapper cd rule-attack-mapper python3 ram.py --rule "SIEM_RULE_SYNTAX" --output mitre_ttp
3. Testing AI Phishing Detection with YARA
yara -r ai_phishing_rules.yar suspicious_email.eml
4. Sec-Gemini CLI for Threat Analysis
curl -X POST https://api.google.ai/sec-gemini/v1/detect -d '{"log":"failed login attempts"}'
5. Running MCP Server Locally
docker run -p 8080:8080 runreveal/mcp-server
6. Darkweb LLM Scraper (Perplexity)
from darkweb_llm import DarkWebCrawler
crawler = DarkWebCrawler(model="llama3-cyber")
results = crawler.search("underground market")
print(results)
7. Microsoft AI Security Agent Deployment
Install-Module -Name MSFTSecurityAI -Force Start-AISecurityAgent -Type "ThreatHunter"
What Undercode Say
AI is reshaping cybersecurity, from automated threat detection to AI-driven phishing. Key takeaways:
– MCP standardizes AI interactions in SecOps.
– AI agents outperform humans in phishing & threat hunting.
– Open-weight LLMs (like Sec-Gemini) enhance SOC efficiency.
– Explainability in AI security tools is now achievable.
Expected Output:
AI-driven SOC analytics enabled. Threats detected: [bash] False positives reduced by [bash]%.
References:
Reported By: Robertauger Ai – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
