AI & Cybersecurity
Explore how artificial intelligence is used to detect cyber threats, prevent online scams, and protect digital systems through real-world examples and hands-on activities.
AI & Cybersecurity gives students a hands-on introduction to both sides of the cybersecurity fight: how attackers use AI to generate phishing emails, deepfakes, and social engineering scams — and how defenders use machine learning to detect and respond to those threats. Students learn core security concepts including confidentiality, integrity, availability, authentication, and common attack patterns such as phishing, weak passwords, and unusual login behavior.
Using Python, students work with real phishing datasets to build AI-powered threat detection models, engineer phishing-signal features, tune classification thresholds, and analyze the tradeoffs between false positives and false negatives. Students also conduct adversarial testing — crafting obfuscated inputs to challenge their own models and then improving them. The program highlights responsible security practices: protecting personal data, understanding privacy tradeoffs, recognizing bias in automated detection, and learning why defensive tools must be used ethically. The program concludes with a team capstone where students choose a threat scenario (phishing, fraud, or impersonation), build a working AI detection solution, and present their findings and prevention recommendations on Demo Day.
Program Details
Start Date
July 13, 2026
End Date
July 24, 2026
Duration
2 weeks
Program Times
5:00 - 7:00 PM PST
Program Cost
$400
Program Information
Prerequisites
- Prior exposure to Python programming, including core concepts (variables, conditionals, loops, functions)
- Comfort with basic algebra and graphs
- Interest in computers, online safety, and problem-solving
Program Format
- Live online sessions 5:00 - 7:00 PM PST
- Daily 2-hour sessions: concept introduction + real-world examples + hands-on coding lab
- Real-world cybersecurity case studies covering both attacker and defender perspectives
- Hands-on Python exercises with guided feedback using real phishing datasets
- Adversarial testing and model improvement activities
- Team collaboration for a capstone project presented on Demo Day
Curriculum Highlights
- Cybersecurity Foundations: CIA triad, common threats, authentication, password hygiene, social engineering, and how attackers use AI to generate phishing emails, deepfakes, and scams
- AI Threat Detection with Python: Build classification models using real phishing datasets, engineer detection features (urgency keywords, suspicious URLs, credential terms), and train ML models to flag threats
- Threshold Tuning & Risk Scoring: Analyze the tradeoff between false positives (blocking safe emails) and false negatives (missing attacks), and justify threshold decisions
- Adversarial Testing & Model Improvement: Craft obfuscated inputs to challenge detection models, perform error analysis on misclassified samples, and iterate with improved features or alternative models
- Responsible Security & Privacy: Discuss privacy vs. surveillance, bias in AI security systems, accountability for AI decisions, and ethical boundaries in defensive security
- Capstone Project: Choose a threat scenario (phishing, fraud, or impersonation), build an AI detection solution, test against edge cases, and present on Demo Day
Capstone Project & Showcase
Students will complete a team-based capstone project where they choose a cybersecurity threat scenario, build a working AI-powered detection solution in Python, and defend their design decisions.
Meet the Instructors
Anjana brings a strong background in data and AI education, with experience guiding high school students through hands-on projects that connect technical skills to real community needs. She helps students frame meaningful questions, work with real-world datasets, and communicate findings with clarity and confidence. Anjana is passionate about creating an inclusive, supportive classroom where students feel empowered to learn and contribute positively.
Priya brings over 25 years of industry experience across a wide range of technology domains, with a specialization in AI and data. She has worked extensively with large-scale datasets and complex analytical concepts, translating technical work into practical, real-world applications. With more than a decade of teaching experience, she has taught students computer science, Python, AI, and data science, supporting learners at different skill levels. Priya is passionate about working with students to build confidence, curiosity, and responsible problem-solving skills through engaging, hands-on projects.
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