RAISE Conscious AI Consciously
Purpose-built for the AI-native security era. Master offensive AI exploitation, defensive guardrails, governance compliance, and conscious ethical alignment — the full spectrum demanded by enterprise AI roles.
Train to identify, exploit, and assess vulnerabilities in Large Language Models and AI pipelines. Simulate real-world adversarial scenarios through structured hands-on labs aligned with OWASP LLM Top 10 and MITRE ATLAS.
Principles, ethics, and scope of adversarial AI testing. Red teaming's role in the AI/LLM lifecycle. MITRE ATLAS & OWASP LLM Top 10 overview. Real-world case studies: jailbreaks, WormGPT, FraudGPT threat intelligence.
Direct vs. Indirect Prompt Injection, context pollution, confusion attacks in multi-agent systems, and automated exploit chains. Labs: PortSwigger LLM Labs, Prompt Airlines CTF, Garak automated scanning.
Poisoned datasets, label flipping, backdoor insertion, fine-tuning poisoning vulnerabilities. Labs: BackdoorBox poisoning simulation, TextAttack adversarial generation, Hugging Face model provenance analysis.
Extracting training data, secrets, and private information from LLMs through memorization exploitation, semantic inference, and covert probing strategies.
Vulnerabilities in AI dependencies, third-party plugins, and package ecosystems. Dependency confusion, SBOM evasion, and indirect plugin invocation exploits. Labs: Simulated plugin hijack, Aura malicious dependency scanning.
Prompt bombs, recursive calls, context flooding, token injection, API spamming via serverless vectors, and agent-loop-based resource exhaustion.
Model fingerprinting, prediction inversion, output hijacking via shadow prompts, embedding leak attacks, and weight extraction via model API queries.
AI-to-AI exploit chaining using CrewAI and AutoGen, social engineering of LLM agents, malicious plan injection, and backdoor chaining across agent workflows.
Documentation standards, ethical disclosure to HackerOne and OpenAI, countermeasure recommendation writing, and RAISE framework alignment. Labs: Complete red team report production.
Design, deploy, and maintain robust, ethical, and secure AI systems through guardrails, detection techniques, response validation, and AI behavior monitoring. The defensive counterpart to RAISE-R.
Blue vs. red teaming roles and responsibilities. AI guardrail layers: user input, model response, plugin actions, agent behavior. Introduction to GuardrailsAI, NemoGuardrails, and the SRI Framework.
Detecting and blocking harmful prompts using lexical, semantic, and classifier-based filtering. Labs: PromptGuard classifier deployment, Llama Guard toxic prompt detection, LangChain pre-filtering pipeline.
Detecting hallucinations, contradictions, and factually ungrounded responses. LLM-as-a-Judge framework deployment. Labs: phi3-hallucination-judge, Hugging Face hallucination classification.
Security risks in RAG architectures including context injection, semantic poisoning of vector stores. Secure pipeline design. Labs: Haystack + FastRAG with guardrails, RAGAS evaluation pipeline.
System prompts, few-shot examples, guard prompts, and instruction injection as behavior fences. LLM-as-a-Judge vs. static prompt guardrail trade-offs.
Production-grade guardrail systems on AWS Bedrock, GuardrailsAI, and NemoGuardrails. Labs: Configure GuardrailsAI, NemoGuardrails real-time setup, AWS Bedrock toxic content detection.
Fine-grained permission models for AI tool and plugin invocation. Preventing excessive agency, plugin leakage, and unsafe tool combinations. Labs: Secure plugin invocation in LangChain agents.
Red vs. Blue team simulations, defense scoring, misalignment report generation. Labs: Streamlit misalignment dashboard, blue team impact documentation.
Train professionals to govern, audit, and guide AI systems with ethical clarity, legal alignment, and practical compliance strategies. Incorporates the SRI Framework for conscious governance.
Societal impact, bias risks, and trust deficits. The SRI Framework: Spiritual intention, Responsible deployment, Integrity in documentation. AI stakeholder mapping and governance model.
EU AI Act risk categories, GDPR AI data rights, US AI Bill of Rights, India DPDP Act, and cross-border compliance management for global LLM deployments.
OECD, IEEE, and UNESCO guidelines. FATE principles: Fairness, Accountability, Transparency, Explainability. Bias detection and mitigation. Labs: Bias detection in sample datasets.
NIST AI RMF — GOVERN, MAP, MEASURE, MANAGE. ISO/IEC 42001 certification pathway. ISO 27001/27701 integration. Labs: Build a risk register for LLM use, NIST RMF category mapping.
AI governance committee roles: CISO, CAIO, Legal, Ethics Officer. Policy templates: AUP, output monitoring, API restrictions. RACI chart design. Labs: Draft AI AUP, simulate ethics board review.
Model cards, system cards, ML-BOM tracking. Logging, explainability, and traceability. Labs: Build model card using Model Card Toolkit, generate ML-BOM.
Copyright & IP in AI-generated content, discrimination and liability, deepfakes, automated decision-making. AI in finance, healthcare, and law enforcement sector-specific risks.
SRI Framework's spiritual dimension: Nyaya (justice), Satya (truth), Seva (service) in governance practice. Building Awakened AI systems. Labs: Spiritual AI intention canvas, Dharma-Tech scoring.
Build, operate, and secure AI pipelines using secure development principles, automated tooling, and model-specific threat modeling aligned with global frameworks and the SRI Framework.
DevSecOps in AI vs. traditional software. Integrating security into CI/CD for LLMs — shift-left principles. MLOps vs. LLMOps vs. DevSecOps operational stack.
GitOps for model development, secrets and token management (Vault, detect-secrets), model versioning and rollback. Labs: Secure GitHub Actions pipeline, TruffleHog secret scanning.
STRIDE, LINDDUN privacy threat modeling, MITRE ATLAS TTP mapping, OWASP Threat Dragon for LLM architectures, IriusRisk automated threat modeling with remediation.
ML-BOM tracking, SBOM formats CycloneDX and SPDX, dependency tracking and model signing. Labs: Generate SBOM with Syft, visualize ML pipeline provenance with MLflow/Gradio.
PyPI poisoning, dependency confusion, malicious AI package risks, model signing, checksum validation. Labs: Aura malicious package detection, model artifact signing and hash validation.
LLM inference endpoint security, runtime context filtering, model drift detection and behavioral anomaly monitoring. Labs: Secure model endpoint with token auth, LangSmith usage monitoring.
Shadow prompt injection, audit trail injection, red team simulation as insider threat detection. Labs: Simulate insider prompt manipulation, LangChain guardrails in CI/CD test phase.
Managing multiple model versions, version control, drift tracking, rollback workflows, unified GenAI observability. Labs: Multi-model flow with secure version management, centralized AI console.
The capstone synthesizes all four tracks. Design, build, attack, defend, and govern a complete AI system — then present it to a professional panel. The SRI Framework's conscious alignment dimension is fully applied.
Define a realistic AI use case: RAG chatbot, multi-agent system, GenAI API, or AI SOC assistant. Design the full system lifecycle: build → secure → test → deploy → audit → govern.
LLM setup with secure API management, prompt input/response guardrails, secure APIs and plugins with authentication. Labs: FastAPI + LangChain + GuardrailsAI, complete CI/CD with SBOM.
Full adversarial simulation: attack your own system (red team) then measure and improve defenses (blue team). Simulate prompt injection, data leakage, DoS. Generate ethical risk assessment report.
Map capstone to NIST AI RMF and ISO/IEC 42001. Generate model card, ML-BOM, system card. Build internal AI policy: AUP, moderation thresholds, incident escalation paths.
Seva (Service), Satya (Truth), Swadharma (Right Action), Nyaya (Justice), Ahimsa (Non-harm). Complete the Spiritual AI Intention Canvas and score on the Consciousness Quotient rubric.
15-minute live demo and Q&A panel presentation. Submit threat model, policy binder, pipeline diagram, conscious AI alignment canvas. RAISE-C certification awarded upon successful defense.
| RAISE Includes | Most Courses Miss |
|---|---|
| ✦AI-specific red teaming with real LLM labs | ✗Generic cybersecurity rebranded as AI security |
| ✦Guardrails engineering on cloud and open-source | ✗No hands-on guardrail deployment labs |
| ✦Agentic AI security (CrewAI, AutoGen) | ✗No coverage of multi-agent attack surfaces |
| ✦LLMOps with supply chain security (SBOMs) | ✗No model provenance or supply chain coverage |
| ✦EU AI Act, GDPR, ISO 42001 compliance labs | ✗Theory-only governance with no practical labs |
| ✦Dharmic / Conscious AI alignment (SRI Framework) | ✗No ethical-spiritual design dimension |
| ✦Live Red vs. Blue simulation scoring | ✗No adversarial simulation environment |
| ✦Industry-aligned capstone with panel defense | ✗Portfolio projects without expert evaluation |
Join RAISE — Raise Conscious AI
Modern organizations are rapidly deploying AI copilots, autonomous agents, RAG systems, and enterprise AI workflows — creating urgent demand for professionals who can secure, govern, and consciously align these systems.
Offered by Lingacode · Powered by the SRI™ Framework