Real-Time Cyber Threat Detection

Explore top LinkedIn content from expert professionals.

Summary

Real-time cyber threat detection involves using advanced tools and technologies to identify and respond to cyber threats as they occur, minimizing potential damage and improving overall security. This approach often leverages AI, machine learning, and endpoint detection and response (EDR) systems for faster, smarter threat management.

  • Utilize AI-powered tools: Implement AI-driven technologies like behavioral analytics and machine learning to identify anomalies and zero-day attacks that traditional methods might miss.
  • Adopt proactive strategies: Regularly monitor for suspicious activity, and combine automated systems with skilled human analysts to ensure comprehensive threat detection and mitigation.
  • Refine incident response: Practice containment workflows such as isolating compromised endpoints and analyzing IoCs to act swiftly during an attack.
Summarized by AI based on LinkedIn member posts
  • View profile for Ridvan Aslan

    Cyber Security Analyst at CYBLU

    3,612 followers

    As a SOC Analyst, I’ve worked with firewalls, SIEMs, email gateways, threat intel feeds… But if I had to pick one tool that truly gives me visibility and power, it’s EDR. Why? Because EDR answers the questions every analyst needs: What exactly happened on this machine? What processes ran? Was there any code injection or persistence attempt? What domains were contacted? Let me share a real-world moment that proved its value: We got an alert about a suspicious executable dropped in a temp folder. Not high severity—but something about it felt odd. So I jumped into our EDR platform and started pivoting: Found the parent process was a renamed PowerShell script Saw the script reached out to an IP flagged in threat intel Tracked lateral movement attempts from that same host Pulled the process tree and hash for further analysis Used the EDR to isolate the host in real-time while we escalated Without EDR, we would’ve been guessing. With EDR, we had clarity, control, and speed. My tips for junior analysts using EDR: Learn how to pivot fast: process > user > IP > domain > file hash Don’t just rely on alerts—hunt in your EDR regularly Use process trees to understand the full scope of an incident Practice containment workflows (isolation, kill process, retrieve file) EDR doesn’t just help you respond — it helps you understand. And in cybersecurity, understanding is half the battle. #Cybersecurity #SOCAnalyst #EDR #EndpointDetection #ThreatHunting #IncidentResponse #BlueTeamTools #RealTimeResponse #CyberDefense #AnalystSkills

  • View profile for Zaara Qadri

    Cyber Operations | Incident Response | SOC Analyst | Advocate of Improvement | Passionate about Cybersecurity | Advocate for Women in Cyber

    5,320 followers

    🔒 AI-Driven SOCs and uses 🔒 As cyber threats evolve, Security Operations Centers (SOCs) must adapt and scale faster than attackers. Lets bring in AI-driven SOCs—leveraging artificial intelligence and machine learning to revolutionize threat detection, incident response, and security analytics. Current SOC Challenges: 👉  High false positives from rule-based detection. 👉  Slow incident response due to manual triage. 👉  Reactive security—only detecting threats after they happen. 🚀 How AI is Transforming SOC Operations: 1. AI-Powered Threat Detection 💥 Behavioral analytics detect insider threats, compromised accounts, and malware beyond signature-based detection. AI-driven EDR and network traffic analysis identify zero-day malware and lateral movement. 2. Automated Incident Response & SOAR 💥 AI prioritizes alerts, reducing false positives and analyst fatigue. Automated playbooks isolate infected endpoints, block malicious IPs, and trigger forensic investigations. 3. Smarter SIEM & Log Analysis  💥 AI enhances Security Information & Event Management (SIEM) by correlating vast security logs and identifying hidden attack patterns. Threat intelligence integration detects Indicators of Compromise (IoCs) in real time. 4. Identity Security & UEBA  💥 AI-driven User & Entity Behavior Analytics (UEBA) detects anomalies like unusual login activity or privilege escalations. Adaptive authentication enforces risk-based MFA based on detected anomalies. 5. Predictive Threat Intelligence  💥 AI scans the dark web for leaked credentials and emerging threats targeting your organization. 💎 Cyber attack prediction models anticipate attack vectors before they happen. An AI-driven SOC isn’t just the future—it’s happening now. AI doesn’t replace analysts; it empowers them by handling repetitive tasks, reducing noise, and surfacing the real threats! 💎 ➡️ Is your SOC leveraging AI yet? Let’s discuss how! 👇 #CyberSecurity #AI #SOC #ThreatDetection #SOAR #SIEM #CyberDefense #ArtificialIntelligence #ThreatIntelligence #MachineLearning #InfoSec #IncidentResponse #SecurityOperations #EDR #UEBA #SOCAutomation #CyberThreats #CyberAttack #ZeroTrust #DarkWebMonitoring #RedTeam #BlueTeam #CyberResilience

  • View profile for Jason Makevich, CISSP

    Founder & CEO of PORT1 & Greenlight Cyber | Keynote Speaker on Cybersecurity | Inc. 5000 Entrepreneur | Driving Innovative Cybersecurity Solutions for MSPs & SMBs

    7,061 followers

    ❌ Stop thinking AI in cybersecurity is only a force for good. You need to know both sides of the coin to stay ahead in today’s threat landscape. 👀 Is this you right now? You hear about AI revolutionizing cybersecurity—automated threat detection, AI-driven firewalls, and machine learning models identifying attacks in real time. You think AI is your ultimate solution. But here’s the truth: AI is a double-edged sword. While it can bolster your defenses, it’s also empowering cybercriminals to launch more sophisticated attacks. 🔑 Here’s the strategy you should adopt to leverage AI for defense while preparing for AI-powered attacks: 1️⃣ Invest in AI threat detection tools → AI can detect anomalies faster than humans. → Equip your systems with AI to recognize threats before they escalate. 2️⃣ Monitor AI-generated threats → Cybercriminals use AI to craft more convincing phishing emails and malware. → Stay informed on emerging AI-powered attack methods to stay one step ahead. 3️⃣ Build a human-AI hybrid defense → AI is powerful, but human expertise is still crucial for analyzing complex threats. → Combine AI capabilities with skilled security professionals to form a resilient defense. 📌 Bonus tip for you: Test your own defenses with AI tools → Use ethical hacking tools powered by AI to simulate potential attacks on your network. → Strengthen your systems by identifying weak spots before cybercriminals do. 👀 Ready to embrace AI as both friend and foe in your cybersecurity strategy? Start by adopting these tools and stay vigilant. The future of cybersecurity is AI-driven, but it’s a race between defense and attack.

  • View profile for Bernard Brantley

    Chief Information Security Officer at Corelight, Inc

    4,219 followers

    Integrating machine learning into cybersecurity is becoming more critical for detecting and mitigating risks. A recent incident where the U.S. seized domains used by an AI-powered Russian bot farm that created fake social media profiles to spread disinformation underscores the escalating threat of AI in cyber warfare. Machine learning algorithms can sift through vast amounts of network traffic data in real time, identifying unusual patterns that might indicate malicious activity. This proactive approach is essential for combating sophisticated cyber threats. For instance at Corelight, we use machine learning to enhance network detection and response, allowing us to pinpoint threats more accurately and reduce false positives. This focus enables security teams to address genuine threats more effectively, enhancing our overall cybersecurity posture. As we navigate these challenges, it's important to discuss how we can further bolster our defenses and stay ahead of these evolving threats. https://lnkd.in/gTxtTk9v

Explore categories