AI in Cybersecurity Operations – Threat Detection & AI-Driven SOC

Defend systems with intelligent vigilance. In this advanced 3-week course, you’ll master AI-powered threat detection, behavioral anomaly identification, and automated incident response—transforming Security Operations Centers (SOCs) from reactive to predictive.

schedule 3 Weeks

security Threat Detection

verified NSTC Verified Cert

monitor_heart AI-Powered SOC Analytics

3.6★

4.8K+ Ratings

4,809+

Cybersecurity Professionals Enrolled

Global

Online Access

play_circle Enroll Now

Part of NanoSchool’s Deep Science Learning Organisation • NSTC Accredited

What You’ll Learn: AI for Proactive Cyber Defense

Move beyond signature-based alerts—deploy AI that sees patterns, predicts breaches, and responds at machine speed.

security

AI Threat Detection Models

Detect zero-day attacks and sophisticated threats using supervised and unsupervised ML.

insights

Behavioral Anomaly Identification

Spot insider threats and compromised accounts through user/entity behavior analytics (UEBA).

bolt

Automated Incident Triage

Reduce alert fatigue with AI that prioritizes, enriches, and suggests responses.

gavel

Ethical & Explainable AI in Security

Ensure transparency, auditability, and bias mitigation in high-stakes security decisions.

Who Should Enrol?

For cybersecurity professionals ready to augment human expertise with intelligent, scalable defense systems.

Cybersecurity Analysts & Threat Hunters

SOC Engineers & Incident Responders

IT Security Managers & CISOs

Security Operations Professionals

Hands-On Cybersecurity AI Labs

Malware Behavior Classifier

Train an ML model to distinguish benign from malicious process behavior using system logs.

UEBA for Insider Threats

Detect anomalous user activity in simulated enterprise authentication and file access data.

Capstone

AI-Enhanced SOC Playbook

Design an end-to-end AI-augmented incident response workflow for your organization.

3-Week Cybersecurity AI Syllabus

~36 hours total • Lifetime LMS access • 1:1 mentor support

Week 1: Foundations of AI in Cybersecurity

Limits of rule-based detection and the rise of ML
Data sources: logs, netflow, EDR, threat intel
Ethical risks: bias, privacy, and adversarial attacks

Week 2: Threat Detection & Anomaly Analytics

Supervised vs. unsupervised threat models
UEBA for user and entity behavior
Reducing false positives with contextual AI

Week 3: SOC Automation & Certification

Integrating AI into SOAR and ticketing systems
Explainability and audit trails for compliance
Certification prep & capstone submission

Frequently Asked Questions

Do I need hands-on experience with SIEM or SOC tools? expand_more

Will I analyze real security logs or threat data? expand_more

Mentor Based

AI in Cybersecurity Operations

Defend Smarter—Harness AI to Revolutionize Cybersecurity Operations

Early access to the e-LMS platform is included

Mode: Virtual / Online
Type: Mentor Based
Level: Moderate
Duration: 3 Weeks

About This Course

AI in Cybersecurity Operations is a practical, expert-led course designed for those working in or aspiring to enter the cybersecurity domain. The course explores how AI and machine learning are transforming the cybersecurity lifecycle—from threat intelligence and anomaly detection to automated response and predictive defense. Participants will learn to apply AI tools and techniques to enhance SOC (Security Operations Center) workflows, identify malicious behavior, and reduce incident response times.

Aim

To equip cybersecurity professionals and IT teams with the knowledge and skills to integrate Artificial Intelligence into security operations, enabling faster threat detection, intelligent response, and robust defense strategies in today’s evolving threat landscape.

Program Objectives

To bridge cybersecurity knowledge with cutting-edge AI methods
To upskill professionals in operational AI tool deployment
To accelerate detection, response, and defense using intelligent systems
To build strategic readiness for AI-integrated cyber threats

Program Structure

Week 1: Foundations of AI and Cybersecurity
Module 1: Cybersecurity Essentials for AI Practitioners

Chapter 1.1: Threat Landscape and Cyber Defense Basics
Chapter 1.2: SOC (Security Operations Center) Workflows and Roles
Chapter 1.3: Common Attack Vectors and Tactics (MITRE ATT&CK)
Chapter 1.4: Data Sources in Cybersecurity (Logs, Alerts, SIEMs)

Module 2: Introduction to AI in Cybersecurity

Chapter 2.1: Why AI? Gaps in Traditional Detection Systems
Chapter 2.2: Key AI Techniques: Anomaly Detection, NLP, and ML Classification
Chapter 2.3: Use Cases – Threat Detection, Alert Triage, and Fraud Prevention
Chapter 2.4: Real-World Case Studies – AI vs. Human Analysts

Week 2: Building AI Models for Security Operations
Module 3: Data-Driven Threat Detection

Chapter 3.1: Collecting and Preprocessing Security Data
Chapter 3.2: Feature Engineering for Network and Log Data
Chapter 3.3: Unsupervised Learning for Anomaly Detection
Chapter 3.4: Supervised Learning for Malware and Intrusion Detection

Module 4: AI Pipeline Design for SOCs

Chapter 4.1: Model Integration into SOC Tooling (SIEM, SOAR)
Chapter 4.2: Alert Prioritization and Noise Reduction Using ML
Chapter 4.3: Real-Time Threat Intelligence with NLP
Chapter 4.4: Model Evaluation and False Positive Reduction Strategies

Week 3: Operationalizing and Governing AI in Cybersecurity
Module 5: Automation, Response, and AI Agents

Chapter 5.1: AI-Driven Incident Response and Playbooks
Chapter 5.2: Security Orchestration, Automation, and Response (SOAR) Systems
Chapter 5.3: GenAI and LLMs in Cyber Operations (e.g., Log Analysis, Report Writing)
Chapter 5.4: Autonomous Threat Hunting and AI Co-pilots

Module 6: Risk, Compliance, and Future Trends

Chapter 6.1: Governance and Compliance in AI-Supported Security
Chapter 6.2: Ethical Challenges in Automated Defense Systems
Chapter 6.3: Adversarial ML in Cybersecurity
Chapter 6.4: Future Outlook – AI Arms Race and Evolving Threats

Who Should Enrol?

Cybersecurity analysts, SOC engineers, network and system administrators
AI/ML practitioners interested in cybersecurity applications
Professionals and students with backgrounds in computer science or IT
Basic knowledge of security concepts and Python recommended

Program Outcomes

Understand and apply machine learning techniques to security data
Integrate AI tools into SOC environments for detection and response
Automate threat classification and anomaly detection tasks
Evaluate and deploy AI-powered security platforms
Balance innovation with responsible, explainable AI practices

Fee Structure

Discounted: ₹21499 | $249

We accept 20+ global currencies. View list →

What You’ll Gain

Full access to e-LMS
Real-world dry lab projects
One-on-one project guidance
Publication opportunity
Self-assessment & final exam
e-Certificate & e-Marksheet

Join Our Hall of Fame!

Take your research to the next level with NanoSchool.

Publication Opportunity

Get published in a prestigious open-access journal.

Centre of Excellence

Become part of an elite research community.

Networking & Learning

Connect with global researchers and mentors.

Global Recognition

Worth ₹20,000 / $1,000 in academic value.

Need Help?

We’re here for you!

(+91) 120-4781-217

★★★★★

AI-Powered Multi-Omics Data Integration for Biomarker Discovery

Great course. Thank you very much.

Abdul Mueed Hafiz • 11/25/2025 at 2:55 pm

★★★★★

AI and Automation in Environmental Hazard Detection

As I mentioned earlier, the mentor’s English was difficult to understand, which made it challenging to follow the training. A possible solution would be to provide participants with a PDF version of the presentation so we could refer to it after the session. Additionally, the mentor never turned on her camera, did not respond to questions, and there was no Q&A session. These factors significantly reduced the quality and effectiveness of the training.

Anna Malka • 07/11/2025 at 5:39 pm

★★★★★

AI and Ethics: Governance and Regulation

Thank you for your efforts

Yaqoob Al-Slais • 09/01/2024 at 1:53 pm

★★★★★

AI Applications in Pharmacy: Leveraging Technology for Innovative Healthcare Solutions

estryuj

Ankita Srivastava • 08/01/2024 at 2:43 pm

View All Feedbacks →

AI in Cybersecurity Operations – Threat Detection & AI-Driven SOC

What You’ll Learn: AI for Proactive Cyber Defense

Who Should Enrol?

Hands-On Cybersecurity AI Labs

Malware Behavior Classifier

UEBA for Insider Threats

AI-Enhanced SOC Playbook

3-Week Cybersecurity AI Syllabus

Week 1: Foundations of AI in Cybersecurity

Week 2: Threat Detection & Anomaly Analytics

Week 3: SOC Automation & Certification

NSTC‑Accredited Certificate

Frequently Asked Questions

Cybersecurity AI Mentors

What Security Leaders Say

AI in Cybersecurity Operations

About This Course

Aim

Program Objectives

Program Structure

Who Should Enrol?

Program Outcomes

Fee Structure

What You’ll Gain

Join Our Hall of Fame!

Publication Opportunity

Centre of Excellence

Networking & Learning

Global Recognition

Need Help?