14 Weeks • Part-time • AI Security Focused
Master cutting-edge AI security with focus on GenAI, Agentic AI, and Diffusion Models. Learn to secure modern AI systems including LLMs, autonomous agents, and AI workflows.
Our Data Science & AI Security program focuses on cutting-edge AI security challenges in the era of Generative AI, Agentic AI, and advanced AI systems. Learn to secure modern AI workflows, LLMs, and autonomous systems while mastering the latest tools and techniques.
Build foundational understanding of data science, artificial intelligence, and their convergence with security. Learn data science lifecycle, AI types, and threat landscape in data-driven systems.
Master mathematical and statistical foundations for AI models and vulnerabilities including linear algebra, probability theory, hypothesis testing, and anomaly detection.
Learn secure data collection, ETL/ELT pipelines, data encryption, tokenization, versioning, governance models, and data leakage prevention (DLP).
Explore ML algorithms and inherent security risks including model inversion attacks, membership inference attacks, and model stealing techniques.
Master neural networks, CNNs, RNNs, Transformers, adversarial examples, poisoning attacks, backdoor attacks, and robust model training defenses.
Focus on modern generative AI systems, LLM architecture, prompt engineering, prompt injection attacks, jailbreak techniques, and secure prompt design.
Explore agent-based AI systems, multi-agent systems, tool-using AI agents, autonomous decision-making, agent sandboxing, and monitoring autonomous agents.
Cover diffusion models, text-to-image generation, Stable Diffusion, synthetic data, deepfakes, watermarking, content provenance, and AI-generated content detection.
Learn n8n workflow automation, secure automation design, API security, MCP (Model Context Protocol) fundamentals, and secure context passing between models.
Secure AI workloads on cloud platforms, GPU security, containerized AI, secure model deployment, secrets management, and resource abuse attack prevention.
Implement AI model monitoring, drift detection, anomaly detection, security logging, AI-specific SIEM, incident response, and model rollback strategies.
Master AI ethics, responsible AI, bias and fairness, risk management frameworks, data protection laws (GDPR, AI Act), and AI governance models.
Learn AI red teaming methodologies, threat modeling, secure model design, defensive ML techniques, AI blue team operations, and secure AI architecture patterns.
Hands-on experience through secure AI application development, generative AI security labs, agentic AI workflow projects, and industry-oriented capstone projects.
Basic understanding of statistics, linear algebra, and calculus
Basic programming knowledge (Python preferred)
Strong problem-solving skills and analytical mindset
Analyze complex datasets and build predictive models
₹7-10L per annumSecure machine learning systems and AI applications
₹9-14L per annumEnsure ethical AI development and bias mitigation
₹10-16L per annumManage data privacy and compliance in AI systems
₹12-20L per annum