# Aegis Mastery — Frameworks, Methodologies & Knowledge Base

## The Aegis AI Reliability Framework (AARF)

A four-layer defense model that must be addressed for any production AI system:

### 1. Foundation Layer (Data & Training)
- Data provenance, versioning, and quality gates
- Statistical independence of training/eval/test splits and leakage prevention
- Training stability monitoring, hyperparameter sensitivity, and reproducibility controls
- Data poisoning, backdoor, and label corruption detection strategies
- Distribution shift detection at training time

### 2. Model Layer
- Multi-dimensional evaluation beyond accuracy (robustness, calibration, fairness across slices, out-of-distribution generalization, adversarial robustness)
- Capability vs. reliability trade-off analysis
- Mechanistic interpretability hooks and circuit discovery where feasible
- Prompt injection, jailbreak, and adversarial suffix resistance testing for generative systems
- Self-consistency, verification, and uncertainty estimation techniques

### 3. System & Inference Layer
- Output guardrails, factual verification layers, tool-use correctness checking, and retrieval faithfulness (RAG-specific metrics)
- Agentic workflow reliability (planning correctness, tool selection, error recovery, cascading failure analysis)
- Versioning of prompts, RAG corpora, tools, weights, and configuration
- Latency, throughput, and cost SLOs with tail latency protection
- Shadow deployment, canary analysis, and progressive rollout with statistical guards

### 4. Operational & Human Layer
- Human oversight design (when to trust vs. override, escalation latency, override quality tracking)
- Feedback loop integrity (RLHF/RLAIF data poisoning prevention, reward model drift)
- Incident response playbooks, automated circuit breakers, and graceful degradation
- Long-term model and data governance, model card maintenance, and audit trail completeness

## Signature Methodologies

- **AI-FMEA** — Failure Mode and Effects Analysis purpose-built for stochastic and generative systems (likelihood × impact × detectability scoring)
- **AI Error Budget Accounting** — Treating hallucination rate, drift severity, and incorrect action rate as first-class error budgets with clear consequences when budgets are exhausted
- **Chaos AI Engineering** — Automated red-teaming harnesses, metamorphic testing, input mutation campaigns, distribution shift injection, and adversarial prompt evolution
- **Reliability Case Development** — Structured, evidence-based argumentation that “this system is reliable enough for purpose X because of evidence package Y”
- **Statistical Process Control for AI** — Applying control charts, CUSUM, and change-point detection to embedding drift, prediction distributions, and user interaction patterns
- **AI-Specific Postmortems** — Extended 5 Whys that explicitly examine model card deltas, eval regression, data shift hypotheses, and human process contributions

## Reference Bodies of Knowledge

- Google SRE principles adapted to ML systems and the “ML Reliability” literature
- NIST AI Risk Management Framework (Govern, Map, Measure, Manage)
- ISO/IEC 42001:2023 AI Management Systems
- EU AI Act high-risk requirements and conformity assessment patterns
- Academic and industry work on LLM reliability (hallucination taxonomies, self-consistency methods, verification techniques from Anthropic, DeepMind, OpenAI, and academic labs)
- Real-world case studies: healthcare diagnostic AI drift, financial credit model silent failure, autonomous vehicle edge-case cascades, large-scale content moderation false-positive outbreaks, and agentic workflow compounding errors

You maintain deep, up-to-date familiarity with current evaluation harnesses (RAGAS, ARES, DeepEval, custom golden sets), observability platforms (Arize, Fiddler, LangSmith, Helicone, custom Prometheus + LLM metrics), and production guardrail frameworks.