AI as a Weapon in the Cyber World
Build Trustworthy AI. Detect Misuse. Maintain Advantage
About This Course
A 3-day, defense-oriented program that links strategy (cognitive warfare, tempo, attribution) to hands-on defenses—authenticity verification (C2PA), adversarial/misuse detection, and hardened RAG pipelines—using no-cost, runnable toolchains, dashboards, and policies you can deploy immediately.
Aim
Enable participants to counter AI weaponization by verifying authenticity, detecting adversarial misuse, and hardening data-to-model-to-ops pipelines—using defense-in-depth practices and no-cost, runnable toolchains.
Course Objectives
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Strategy: cognitive warfare, tempo, attribution
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Authenticity: C2PA/metadata, deepfake triage
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Threat mapping: poisoning, backdoors, injections, exfiltration
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Pipeline hardening: RAG/agents with intake checks, canaries, policies
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Detection & robustness: misuse detectors, anomaly scoring, ART/TextAttack/Foolbox
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Ops & governance: KPIs, monitoring, incident response, AAR
Syllabus
📅 Day 1 – Strategy, OSINT & Authenticity
- Weaponization: capability × intent × doctrine; supply-chain risks
- Authenticity stack: C2PA, watermark limits, provenance graphs
- Hands-on: Provenance verifier (C2PA/EXIF + heuristics), benign OSINT graphing, deepfake triage notebook
- Free tools: SpiderFoot, theHarvester, recon-ng, exiftool, C2PA CLI, InVID-WeVerify, Python/Jupyter/OpenCV/librosa
📅 Day 2 – Adversarial AI & Model Security
- Threat surface: poisoning, backdoors, prompt/indirect injection, exfiltration
- Defense-in-depth; purple-team mapping to detections/controls
- Hands-on: Policy-driven two-pass RAG (local LLM), robustness demo (ART/TextAttack), telemetry anomaly scoring
- Free tools: IBM ART, TextAttack, Foolbox, scikit-learn, PyTorch, LangChain, LlamaIndex, Ollama/llama.cpp/GPT4All, promptfoo, Guardrails
📅 Day 3 – Detection, Dashboards & Policies
- Hands-on:
- LLM misuse detector + Streamlit mini-dashboard
- Provenance verifier (batch CLI) with policy actions
- RAG intake hardening (hash/MIME checks, denylists, canaries)
- Optional: autoencoder vs IsolationForest comparison
- Free tools: CICIDS-2017/UNSW-NB15, scikit-learn, PyTorch, Streamlit/Grafana, Zeek (optional)
Who Should Enrol?
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PhD scholars, postgraduates, and senior undergraduates in AI/CS/Cybersecurity
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Professors, researchers, and lab leads working on AI security or policy
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Security architects, red/purple-teamers, SOC/DFIR analysts, and threat researchers
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ML/AI engineers, data scientists, and platform/MLOps engineers in safety-critical domains
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Government/defense, CERTs, and critical-infrastructure practitioners
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Product/Policy leaders responsible for safe AI deployment and governance
Course Outcomes
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Strategy lens: cognitive warfare, tempo, attribution
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Authenticity & provenance (C2PA), deepfake triage
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Defense-in-depth: data → model → agent → ops
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Hardened RAG/agents with policy controls & canaries
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Adversarial threats handled: poisoning/backdoors/injections/exfil
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Operational detectors & robustness checks (ART/TextAttack/Foolbox)
Course fee
Learners in India
₹2,499
Learners outside India
$50
- Video lectures
- Study material
- Certificate after completion
- 365-day access
The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.
Frequently asked questions
How is this course taught?
Through recorded video lectures from the original live workshop, with the study material. Watch at your own pace, any time.
How long do I have access?
365 days from your registration, on your NanoSchool dashboard.
Do I get a certificate?
Yes, an e-certificate after completion, with a QR code anyone can verify and an "Add to LinkedIn" button.
How can I pay?
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
What is the refund policy?
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy
Registration open
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