Six Core Modules

Module 1 core module AI→Sec

Software Security: Program Understanding, Vulnerability Discovery and LLM-Guided Fuzzing

• Cross-arch function similarity
• LLM-assisted decompilation
• Taint (static/dynamic/hybrid)
• LLM-guided fuzzing (seeds/grammars, NL oracles)

Outcome: Build semantic visibility into code, automate vulnerability discovery, and prioritize findings.

Module 2 core module AI→Sec

Runtime Monitoring in an Encrypted World: Logs, Telemetry & Network Flows

• Log anomaly (sequence models)
• Cross-source correlation
• Encrypted traffic inference
• Flow/timing fingerprinting
• DFIR timelines & reporting

Outcome: Detect and explain sophisticated attacks using logs-as-language and payload-blind flow analysis.

Module 3 core module AI→Sec

Malware Analysis with Deep Learning & Transformers

• Byte/IR embeddings (PE/ELF)
• Syscall/behavior sequences
• Family clustering/linkage
• Packing/obfuscation robustness
• LLM assist for triage

Outcome: Classify, cluster, and attribute malware using learned representations of static and dynamic behavior.

Module 4 core module AI→Sec

Side-Channel, Hardware, and ICS/IoT Security

• CNN/Transformer SCA pipelines
• Masking/shielding/randomization
• ICS anomaly detection
• Device identity & attestation
• Wireless/RF fingerprinting

Outcome: Apply ML to power/EM/timing/RF streams and secure industrial/embedded environments.

Module 5 core module Dual

Agentic SecOps & Automation

• Agent tool-use & approvals
• CTI → ATT&CK/TTPs → detections
• Playbooks/orchestration budgets
• BAS/purple teaming with LLMs
• Auditability & connectors

Outcome: Automate high-volume tasks safely while keeping analysts in control.

Module 6 core module Sec→AI

Guardrails & Security of Agentic AI Systems (Model/LLM Security)

• Prompt/tool injection & jailbreaks
• Data poisoning & deceptive fine-tuning
• Model/data exfiltration
• Least-privilege tools & isolation
• Red teaming & governance

Outcome: Secure models/agents and limit the blast radius of automated actions.