AI in Cybersecurity Intern
Detect threats, analyze cyberattacks, and build AI-powered security tools before you graduate — become the cybersecurity engineer companies urgently need.
- Learn Cybersecurity + AI Integration
- Build Real AI Security Tools
- Mentorship from Cybersecurity Experts
- Placement-Oriented Program
About Program
The AI in Cybersecurity Intern Program is designed for final-year VTU students who want to build practical skills in both cybersecurity and artificial intelligence. The program starts with foundational security concepts including networking, vulnerabilities, attacks, threat analysis, and defensive mechanisms, then progresses into applying AI for threat detection, phishing classification, anomaly detection, malware analysis, and SOC automation. Students work with real-world datasets, tools, and workflows used by SOC teams. Through guided mentorship, hands-on labs, and projects, learners gain the expertise required for modern cybersecurity roles. By the end, students create 2–3 portfolio-ready AI-powered security tools and are job-ready for cybersecurity and AI-driven defense roles.
Key Features
Program Content
Module 1 – Networking, Linux & Security Basics
- OSI & TCP/IP layers
- LAN, WAN, routing & switching basics
- IP addressing, subnetting, DNS, DHCP
- Linux essentials for cybersecurity
- Users, permissions, SSH & remote access
- Firewalls, IDS vs IPS
- Introduction to common cyber threats
- Understanding CVEs & vulnerabilities
Module 2 - Fundamentals of Cybersecurity & Ethical Hacking
- Confidentiality, Integrity, Availability (CIA triad)
- Types of attacks: phishing, malware, MITM, DoS
- Cyber kill chain & attack lifecycle
- Web app security basics (OWASP Top 10)
- Password cracking basics
- Network sniffing & packet analysis
- Understanding logs & system events
- Security controls & best practices
Module 3 - Python Programming for Cybersecurity
- Python basics: variables, lists, dictionaries
- File handling for log parsing
- Using libraries: pandas, numpy, re
- Parsing logs, emails & web data
- Creating automation scripts
- Handling JSON/XML security feeds
- API consumption using Python
- Error handling in security scripts
Module 4 - Introduction to AI & ML for Cybersecurity
- What is ML? Classification, clustering, anomaly detection
- Supervised vs unsupervised learning
- NLP basics for security
- Model evaluation: accuracy, precision, recall
- Understanding datasets used in security
- Ethical considerations in AI for cyber defense
- Basic ML workflows for threat detection
- Using AI safely in cybersecurity
Module 1 - AI for Threat Detection (ML + NLP)
- Data preprocessing for cyber datasets
- Tokenization, stemming, lemmatization
- Feature extraction for text classification
- Training phishing email classifiers
- Spam detection workflows
- Evaluating classification models
- Handling imbalance in datasets
- Improving model performance
Module 2 - Network Traffic Analysis & Anomaly Detection
- Understanding PCAP files
- Extracting traffic features
- Detecting anomalies using ML
- Clustering techniques for intrusions
- Building IDS-like models
- Using Scikit-learn for anomaly detection
- Deploying models for real-time analysis
- Visualizing patterns & trends
Module 3 - Malware Analysis & Classification (AI-Powered)
- Types of malware: ransomware, trojans, spyware
- Static vs dynamic analysis
- Extracting features from malware samples
- Building malware classifiers
- Signature-based vs AI-based detection
- API behavior monitoring
- Safe malware dataset handling
- Incident response fundamentals
- Types of malware: ransomware, trojans, spyware
Module 4 - Security Automation & SOC Workflows
- Automating log analysis
- Using AI for alert triage
- Detecting abnormal login patterns
- Email header analysis automation
- SIEM concepts: Splunk/ELK basics
- Case management workflows
- Playbooks & response outlines
- Integrating security automation into SOC operations
Module 5 - Cloud Security & Identity Monitoring (Intro)
- Understanding IAM & access control
- Cloud logging basics
- Detecting suspicious cloud activity
- Securing cloud workloads
- Cloud misconfigurations & risks
- Cloud event monitoring automation
- Multi-factor authentication systems
- Access pattern analysis using AI
Module 1 - Product Thinking for Security Tools
- Understanding enterprise security challenges
- Identifying high-impact security problems
- Designing intuitive security dashboards
- UI/UX for threat intelligence tools
- Visual mapping of attacks & detections
- Creating prototypes & system flows
Module 2 - AI + Cybersecurity Use Cases in Industry
- SOC automation
- Fraud detection in BFSI
- AI-powered email security
- Secure coding & AI code analysis
- AI for vulnerability prioritization
- Using AI to simulate attack scenarios
- Preparing pitch decks for AI security tools
Module 3 - Career Growth & Professional Skills
- Resume for cybersecurity roles
- LinkedIn branding for security careers
- GitHub project portfolio
- Interview prep (security + AI)
- Documentation & reporting
- Presentation skills for demos
- Professional communication
Mini Projects (Examples)
- Phishing email classifier
- Log parsing & alert generator
- Suspicious IP detection tool
- URL reputation analyzer
Concepts Covered:
- Feature extraction
- Python scripting
- ML basics
- Log automation
Intermediate Projects (Examples)
- Network intrusion detection (anomaly-based)
- Malware behavior classifier
- Email header analyzer using NLP
- Cyber threat intelligence dashboard
Concepts Covered:
- PCAP analysis
- Clustering
- Dataset modeling
- SOC workflows
- Visualization
Capstone Projects (Examples)
- AI-powered SOC assistant
- Full-scale phishing detection system
- Malware detection & response automation
- Enterprise Threat Monitoring & Alerting System
Concepts Covered:
- ML pipelines
- Automation
- Dashboards
- Deployment
- Documentation
Tools & Softwares
Why Choose This Internship
Salary Scale
Job Role
- AI Security Analyst
- SOC Analyst
- Cybersecurity Analyst
- Security Automation Engineer
- Network Security Analyst
- Incident Response Associate
- Fraud Detection Analyst
- AI Threat Modeling Engineer
FAQ's
Yes. You'll receive VTU-compliant certificates and documentation.
No. The program starts from fundamentals and scales gradually.
AI-based phishing classifiers, anomaly detectors, malware classifiers, and a full AI-driven security monitoring system.
Yes — resume, LinkedIn, GitHub, mock interviews & job guidance.
Offered in both offline and hybrid formats.
Yes, you will receive a verified completion certificate from Rooman Technologies upon meeting all requirements.
Absolutely. The curriculum is beginner-friendly and AI-assisted.
Python, Scikit-learn, Wireshark, Splunk basics, AI APIs, and automation tools
Contact Us
Have questions about our programs or need guidance? Reach out to us and we’ll be happy to help.
Email Us
online@rooman.net
Call Us
08069451000
Visit Us
Rooman Technologies, Bangalore, India