Kavi's_Network
// LIVE WEBINAR — MARCH 08, 2026
ML models analyse millions of events/sec to spot anomalies rule-based systems miss. Uses supervised classification, unsupervised clustering for zero-days, and graph neural networks for lateral movement. Tools: Darktrace, Vectra AI, CrowdStrike Falcon.
SOAR platforms auto-isolate endpoints, revoke credentials, block IPs — all without human intervention. MTTR drops from hours to seconds. Platforms: Palo Alto XSOAR, Splunk SOAR, IBM QRadar SOAR.
Next-gen SIEM (Microsoft Sentinel, Splunk ES) correlates events across thousands of sources, reduces alert fatigue by 90%, and parses unstructured logs with NLP.
AI agents learn baseline behaviour and flag deviations — file access, process trees, network calls. SentinelOne and CrowdStrike autonomously kill malicious processes before analysts see the alert.
NLP transformers scan for social engineering, impersonation, brand spoofing, and malicious intent. AI learns from user feedback. Vendors: Proofpoint, Abnormal Security, Mimecast.
AI profiles normal activity (login times, data access, geo-location) and scores anomalies. Critical for insider threats, compromised accounts, and privilege escalation.
AI moves the SOC from reactive to autonomous. Tier-1 tasks — triage, enrichment, investigation — fully automated. Google Chronicle + Gemini, Microsoft Copilot for Security.
"Never trust, always verify" — AI makes it scalable. Continuously evaluates risk for every access request based on device health, behaviour, location, and threat intel.
LLMs read, summarise, correlate thousands of threat reports, CVEs, dark web posts. Extract IOCs, map to MITRE ATT&CK TTPs, brief analysts in plain English.
AI prioritises thousands of CVEs by cross-referencing assets, exposed services, exploitability. The 3% of vulns that cause 90% of risk. Tools: Tenable One, Qualys TruRisk.
AI forecasts attack vectors before they materialise by analysing historical patterns, threat actor TTPs, geopolitical signals, dark web chatter.
CSPM tools continuously scan for misconfigurations, exposed buckets, overprivileged IAM, and auto-remediate. Tools: Wiz, Orca Security, Prisma Cloud.
Attackers craft adversarial inputs to fool ML models. Imperceptible pixel changes cause misclassification. Dangerous for AI malware scanners and facial recognition.
Inject malicious data into training to corrupt models. A poisoned IDS ignores attacker TTPs permanently — a backdoor in the defence.
Prompt injection hijacks AI via hidden instructions in documents/emails. Can exfiltrate data, bypass controls, or execute unintended actions in agentic AI.
Model extraction: reconstruct a copy by querying the API. Model inversion: extract private training data. Bypasses traditional security controls.
Attackers operate just below detection thresholds. Fragment traffic, use LOLBins, vary timing, mimic legitimate behaviour to defeat AI anomaly detection.
Compromise AI models during distribution — backdoors in HuggingFace models, PyPI packages, containerised ML pipelines.
AI voice/video impersonation. The $25M Hong Kong incident (2024) — deepfake video call impersonating CFO. Real-time voice cloning in calls.
Polymorphic malware rewrites its own code per infection, evading signature AV. WormGPT, FraudGPT — dark web LLMs for malicious code.
LLMs generate 10,000 unique, contextually relevant phishing emails in minutes. 3-5× higher success rates than generic phishing.
3-10 seconds of audio → near-perfect voice clone. Used to bypass voice authentication at banks and impersonate IT helpdesk.
Autonomous ransomware: selects targets, scans vulns, moves laterally, times encryption, and negotiates ransoms via AI chatbots.
AI scrapes LinkedIn, GitHub, Shodan, breach data to build detailed attack maps — employee names, tech stack, exposed services — in hours.
Adversarial training and ensemble defences as mitigation. Standard DL-based IDS bypassed with <5% traffic perturbation.
Introduces GOVERN, MAP, MEASURE, MANAGE functions. Gold standard for enterprise AI governance, referenced globally.
Fine-tuned LLMs identify zero-day vulnerabilities in C/C++ with precision comparable to expert auditors. Dual-use concerns.
Living knowledge base of TTPs specific to ML systems. Real-world AI attack case studies. Essential for red teams.
Autonomous cyberweapon creation, CBRN uplift risk, agentic AI safety failures. Model evaluation methodology now adopted by major labs.
Top AI-related threats to European infrastructure. Prompt injection, AI supply chain attacks, deepfake fraud. Aligned with EU AI Act.
Build/secure AI/ML pipelines, audit models, implement MLSecOps. Certs: AWS ML Specialty, GIAC GAISC
Adversarial testing of AI — prompt injection, model extraction, poisoning. Certs: OSCP, CEH, MITRE ATLAS
AI-augmented threat hunting, actor attribution, dark web monitoring. Certs: GCTI, CompTIA CySA+
Security architecture for AWS/Azure/GCP hosting AI workloads. Certs: AWS Security Specialty, CCSP
Embed security into CI/CD — SAST, DAST, SCA, container security. Certs: Certified DevSecOps Professional
Navigate NIST AI RMF, EU AI Act, ISO 42001. Certs: CISA, CRISC, AIGP
Discover zero-days in AI systems. Publish CVEs, write PoC exploits. Certs: OSCP, OSED, GREM
Lead human + AI breach response. Cannot be automated. Certs: GCFE, GCIH, GCFA
SIEM webhook → enrich with VirusTotal, Shodan, AbuseIPDB → score → Slack/Teams. Analysts get pre-enriched, scored alerts.
New IOC → query VirusTotal, Shodan, OTX, GreyNoise, Censys in parallel → populate SOAR ticket in <5 seconds.
Isolate endpoint → revoke tokens → block IP → Jira ticket → PagerDuty → forensic collection. MTTR 4 hours → 4 minutes.
Automated weekly/monthly reports: vuln scanner + patch status + MDM compliance → formatted PDF/Markdown delivered to stakeholders.
Connect to OpenAI/Claude/Ollama: AI reads CVEs, checks your assets, drafts patch recommendations, assigns tasks — autonomously.
docker run -it --rm -p 5678:5678 n8nio/n8n
No — AI will transform roles. Routine tasks automated, but demand for professionals who build/oversee/outsmart AI will surge. 3.5M+ unfilled jobs.
AI replaces tasks, not people. Experts using AI are 10× more effective. Human judgment and adversarial creativity can't be automated.
Start with EDR, AI-SIEM, UEBA, AI email filters. Then build custom automation with n8n and LLM APIs.
Absolutely. Quantum computing, AI-native attacks, IoT, satellites create entirely new security domains requiring human expertise.
Force multipliers, not replacements. Help write reports, debug code, research threats — but can't lead IR teams or make contextual decisions.
Network+ → Security+ → Home lab → Specialise → Cert → Portfolio → Apply.
AI/ML Security, Cloud Security, Zero Trust, DevSecOps, Threat Intel, Adversarial ML, AI Governance. Add Python + n8n.
Alert triage, threat intel enrichment, IR playbooks, compliance reports, AI-agent workflows — all without full custom code.