🧠 AI/ML SecOps

1NAIVE/CLASSIC RAG — The simplest form: embed documents into vectors, retrieve top-K chunks by cosine similarity, stuff into the LLM prompt. Easy to build but limited — no query optimization, fixed-size chunks, high irrelevance rate. Use for simple FAQ bots or internal search.

2ADVANCED RAG — Still vector-based but adds optimizations: query rewriting (LLM rephrases for better retrieval), hybrid search (vector + BM25 keyword), cross-encoder re-ranking, semantic chunking, metadata filtering, and self-reflection (LLM checks if retrieved context is sufficient). Use for production search systems needing high accuracy.

3GRAPH RAG — Uses knowledge graphs (entities + relationships) instead of flat vector search. Enables multi-hop reasoning — traversing entity relationships to answer complex questions like 'Which compliance frameworks require encryption at rest?' The graph links HIPAA→requires→encryption, PCI-DSS→requires→encryption. Use when data has rich relationships between entities.

4AGENTIC RAG — The LLM becomes an autonomous agent that DECIDES how to retrieve. It chooses tools (vector DB, SQL, web search, APIs), plans multi-step retrieval, evaluates results, and iterates. Use for complex research tasks requiring multiple data sources.

5MODULAR RAG — Mix-and-match architecture combining any techniques: vector + graph + agent routing + custom re-ranking. Most flexible but most complex. Use for enterprise systems needing custom pipelines. SECURITY CONSIDERATIONS scale with complexity: Naive faces data poisoning; Advanced adds query manipulation risk; Graph adds graph poisoning; Agentic adds agent autonomy abuse; Modular inherits all risks. Each additional layer increases both capability and attack surface.

• Sources (video transcripts, PDFs, logs) need validation before processing
• For video: extract transcript → chunk → clean
• For logs: parse → normalize → filter sensitive data
• Security: validate input formats, scan for injection payloads in uploaded content, enforce file size/type limits

• Convert chunks into vector embeddings (OpenAI Ada, Gemini, or local models via Ollama)
• Store in vector DB (ChromaDB for local, Pinecone/Weaviate for cloud)
• Security: encrypt embeddings at rest, implement document-level access controls, prevent cross-tenant data leakage in multi-user systems

• Framework choice matters — CrewAI for role-based multi-agent (each agent has a role, goal, backstory), LangGraph for stateful graph workflows (better for complex conditional logic), or Google ADK for enterprise scale
• Key patterns: Manager agent delegates to specialists, ReAct loop for reasoning, and human-in-the-loop for high-risk actions
• Security: scope each agent's tool permissions (least privilege), validate inter-agent messages, implement rate limiting on agent actions

• Query the vector DB, re-rank results, feed relevant context to the LLM
• For complex questions: decompose into sub-queries, retrieve for each, merge results
• Use confidence scoring to determine if more retrieval is needed
• Security: sanitize retrieved chunks before LLM ingestion (indirect prompt injection via poisoned documents), validate query parameters, monitor for abnormal retrieval patterns

• The LLM generates the final answer or takes action (API calls, code execution, alerts)
• For cyber-defense agents: generate threat reports, fire SIEM alerts, trigger containment playbooks
• Security: validate all LLM outputs before action execution, implement approval workflows for destructive actions, log every tool call with full parameters for audit
• The key architectural principle: treat every agent like an untrusted service — authenticate, authorize, validate, log

1If you just need to adapt the model's behavior/style → prompt engineering first (zero cost).

2If prompt engineering isn't enough and you have modest data → LoRA/QLoRA (best cost-performance ratio).

3If you need maximum accuracy on a very specialized domain → full fine-tuning.

4If you need to quickly switch between multiple task specializations → prompt tuning (swap soft prompts). SECURITY CONSIDERATIONS: Fine-tuned models can memorize and leak training data (PII exposure). Always: train on properly sanitized data, test for memorization (canary token test), implement output filtering, and never fine-tune on data you wouldn't want the model to reproduce.

1Token Embeddings — convert words into numerical vectors.

2Positional Encoding — since transformers have no inherent notion of order, position information is added (sinusoidal or learned).

3Multi-Head Self-Attention — multiple attention 'heads' each learn different relationship patterns (syntax, semantics, long-range dependencies).

4Feed-Forward Networks — process the attention output through non-linear transformations.

5Layer Normalization — stabilizes training.

6Residual Connections — allow gradients to flow through deep networks. AUTOREGRESSIVE MODELS (GPT family): Generate text one token at a time, left-to-right. Each token prediction depends on all previous tokens. Pros: excellent at coherent, flowing text. Cons: inherently sequential at inference time (can't parallelize generation), and tendency toward repetitive or degenerate outputs. DIFFUSION-BASED LLMs (LLaDA, MDLM): A fundamentally different approach — instead of predicting one token at a time, the model starts with fully masked/noisy text and gradually 'denoises' it into coherent language, similar to how image diffusion models work (Stable Diffusion, DALL-E). Process: Start with [MASK] [MASK] [MASK]... → gradually unmask tokens in any order → final clean text. Pros: can generate all tokens simultaneously (parallelizable), better at capturing global document structure, can 'revise' any position at any step. Cons: still early stage, inference quality catching up to autoregressive. SECURITY IMPLICATIONS:

1Autoregressive models are vulnerable to prefix-based prompt injection — since they generate left-to-right, an attacker can control the 'trajectory' by manipulating the beginning.

2Diffusion LLMs may be more resistant to sequential prompt injection (since they don't process left-to-right), but introduce new risks: the denoising process could be manipulated through adversarial noise patterns.

3Both architectures face: training data poisoning, model extraction attacks, and memorization of sensitive training data.

4For security practitioners: understanding the generation mechanism matters for designing effective guardrails — a guardrail designed for autoregressive output may not work for diffusion-based output.

1Threat Detection — ML models baseline normal behavior and detect anomalies that signature-based tools miss (zero-day attacks, insider threats).

2Alert Triage — NLP and classification models auto-categorize and prioritize alerts, reducing false positives by up to 90%.

3Incident Response — SOAR platforms with AI can automatically execute containment playbooks (isolate hosts, block IPs) with human approval gates.

4Threat Hunting — LLMs can generate hunt hypotheses, query SIEM data in natural language, and correlate disparate data sources. The key principle: AI augments human analysts, handling volume and speed while humans provide judgment and creativity.

1Adversarial AI — attackers can craft inputs to evade ML detection models.

2False confidence — over-reliance on AI decisions without human verification.

3Data quality — ML models are only as good as their training data; biased or incomplete data leads to blind spots.

4Model drift — threat landscapes change faster than models can adapt without continuous retraining.

5Explainability — black-box models make it hard to understand why an alert was generated or suppressed.

6Alert fatigue transfer — AI may reduce volume but unfamiliar AI-generated alerts can create new cognitive load. Mitigations: human-in-the-loop, continuous validation, adversarial testing, and model monitoring.

1Start with a Goal — define measurable objectives, choose workflow design pattern, identify HITL points, define constraints.

2Pick the Right Model — LRM for complex reasoning (coding), LLM for general tasks, SLM for routing/rewriting.

3Choose Framework — Simple workflows: Gumloop, n8N, Dify. Production: LangChain, Google ADK, CrewAI, OpenAI Agent SDK.

4Connect Tools — MCP integration, agent-as-tools, functional calling, file system access.

5Divide Memory — Cache memory for current conversations, episodic memory for past events, file system memory for persistent storage.

6Manage Context — compress via summarization, monitor effectiveness with metrics, add context based on need.

7Test and Evals — unit tests for functions/workflows, edge case discovery, cost per successful task.

1Task-Specific — narrow scope, limited attack surface but vulnerable to targeted prompt injection.

2Reactive — no memory to corrupt, but can't detect evolving attacks.

3Model-Based — internal model can be poisoned through crafted inputs.

4Rational — can be manipulated by adversarial inputs making malicious options appear optimal.

5Goal-Based — goal manipulation attacks redirect agent behavior.

6Utility-Based — utility function poisoning changes scoring without detection.

7Multi-Agent — highest risk: inter-agent communication interception, rogue agents, cascading failures.

8Reflex with Memory — memory corruption attacks influence future decisions.

9Planning — plan manipulation compromises all subsequent actions.

0Learning — most vulnerable to data poisoning of learned behavior.

1VIDEO NOTE TAKER — multimodal summarization with vision + language models, security: content filtering, rate limiting.

2REAL-TIME RAG — vector DB management, embeddings, retrieval pipelines, security: document-level access controls, anti-injection.

3DOCUMENT ANALYST — structured data extraction from PDFs/contracts, security: input validation against malicious files, audit logging.

4REASONING APP — Chain of Thought with tool use and self-reflection, security: sandboxed execution, tool permission boundaries, injection chain prevention.

1Pick one boring, repetitive job — define success in one sentence.

2Map steps as SOP — INPUT → ACTIONS → DECISION → OUTPUT, 4-7 steps.

3Choose platform — LangChain, CrewAI, OpenAI SDK for devs; Zapier, n8n for no-code.

4Define inputs/outputs/tools — treat it like an API, attach data, action, and orchestration tools.

5Write job description — system prompt with role, boundaries, style, examples, ReAct pattern.

6Add memory — conversation state + task memory + knowledge memory (vector store).

7Add guardrails — approval for high-risk actions, log every tool call.

8Wrap in simple interface — chat, Slack/Teams, or web form.

9Test on 5 real tasks — trace tool calls, score correctness, steps count, and time saved.

1Programming & Prompting — Python, JS, CoT, Role Prompting, Reflexion Loops.

2AI Agent Basics — Autonomous vs Semi-Autonomous, BabyAGI, CAMEL, MCP, A2A Protocol.

3LLMs & APIs — GPT-4, Claude, Gemini, Llama, Function Calling, Output Parsing.

4Tool Use — File/API/Search/Code tools, Memory Integration.

5Frameworks — LangChain, AutoGen, CrewAI, Flowise, Haystack, Semantic Kernel.

6Orchestration — n8n, Zapier, LangGraph, DAG Management, Event-Driven.

7Memory — Short/Long-Term, Episodic, Vector Stores (Pinecone, Chroma, FAISS).

8RAG — Embeddings, Document Indexing, Hybrid Search.

9Deployment — FastAPI, Docker, K8s, Agent Hosting.

0Monitoring — LangSmith, OpenTelemetry, Auto-Evaluation.

1Security — Prompt Injection Protection, RBAC, Red Team Testing.

1LLM (Brain in a Jar) — text generation only, $ LOW cost, LOW security risk.

2RAG (Brain + Library) — doc retrieval + LLM, $$ MEDIUM cost, MEDIUM risk (injection via docs).

3AI Agent (Brain + Hands) — autonomous tool use, $$$ HIGH cost, HIGH risk (tool misuse, privilege escalation).

4Agentic AI (The Whole Dept) — multi-agent coordination, $$$$ HIGHEST cost, CRITICAL risk (cascading failures, rogue agents). Cost goes up as you add capability.

1Insecure code generation (SQL injection, XSS, hardcoded secrets).

2Dependency risks (vulnerable/hallucinated packages).

3Context leakage (proprietary code sent to LLM APIs).

4Skill atrophy ('it works' ≠ 'it's secure').

5License/IP issues (GPL code in proprietary projects).

6Audit trail gaps (no AI vs human code tracking).

1User Layer — Azure AI Chatbot, M365 Copilot, Power Platform.

2API Gateway — Microsoft Entra ID, OAuth2, RBAC/Zero Trust.

3RAG Pipeline — Document Parsing → Chunking → Embedding → Indexing.

4Model Routing — Mistral, Azure OpenAI, Claude, Local Models.

5Agentic AI — Agent Orchestrator → Azure SQL, Cosmos DB, Cognitive Search, SharePoint.

6Observability — Azure Monitor, Log Analytics, App Insights, Purview.

1Infrastructure — APIs, GPU clusters, data lakes, storage.

2Agent Internet — A2A protocol, embedding stores (Pinecone, Weaviate), agent identity.

3Tooling — LangChain function calling, RAG, code execution, automation.

4Cognition — Planning, decision making, self-improvement, feedback loops.

5Communication — Inter-agent messaging, event-driven coordination.

6Memory — Working/long-term memory, preferences, conversation history.

7Application — Personal assistants, research agents, platform bots.

8Ops & Governance — Deployment, policy engines, logging, trust frameworks.