Practical guides, benchmarks, and best practices for implementing LLM infrastructure on-premise. No marketing fluff—just technical depth.
Direct vs indirect prompt injection, a threat model for on-prem RAG, defanged attack shapes, and a five-layer defense-in-depth strategy mapped to the OWASP LLM Top 10 and MITRE ATLAS.
Why downstream systems must treat model output as untrusted input: XSS, SSRF, SQLi and SSTI sinks, defanged attack shapes, and context-aware output encoding.
Poisoned corpora, RAG backdoors, feedback-loop poisoning, retrieval anomalies, and data-lineage controls for on-prem LLM systems.
Context flooding, KV-cache exhaustion, tool fan-out, token budgets, queue isolation, and detection for vLLM and TensorRT-LLM style serving.
Model weights, tokenizers, adapters, datasets, containers and CUDA/Python dependencies treated as signed, verified release artifacts.
On-prem moves the risk from cloud egress to internal over-sharing. Identity-aware retrieval, DLP, and layered controls to keep PII and secrets in-bounds.
Over-permissioned tools, unsafe schemas, confused-deputy failures, tool-call telemetry, and deterministic policy gates outside the model.
Autonomy levels, permission boundaries, human-in-the-loop review, execution budgets, and safe agent design for high-impact workflows.
Groundedness checks, citation validation, eval suites, reviewer workflows, and decision gates that prevent fluent output from becoming unverified authority.
Weight exfiltration, API extraction, prompt and trace leakage, artifact access control, watermarking, and monitoring for self-hosted inference.
A redacted end-to-end exercise from document ingestion to retrieval, prompt assembly, tool gating, and blue-team evidence capture.
A payload-free taxonomy of guardrail-bypass families with telemetry signals and controls that survive prompt churn.
API extraction and profiling risks for self-hosted inference, with telemetry, throttling, canaries, and response controls.
A SIEM-ready event model for prompts, retrievals, tool calls, guardrails, privacy controls, and illustrative Sigma-style detections.
A practical incident-response runbook for LLM data leaks: triage, containment, evidence, scoping, eradication, and recovery.
Turn prompts, retrievals, tool calls and guardrail verdicts into an LLM SIEM feed. A detection catalogue mapped to MITRE ATT&CK, with illustrative Sigma rules.
Decompose the stack into trust zones, apply STRIDE per component, map to OWASP LLM Top 10 and MITRE ATLAS, and prioritise secure-by-design controls.
Regulation (GDPR, AI Act, NIS2), use cases for banking/healthcare/defense, hidden cloud costs (egress, lock-in), and TCO comparison cloud vs on-prem over 3 years.
Production benchmarks comparing latency, throughput, and filtering capabilities. Decision matrix for choosing the right vector database based on scale, budget, and feature requirements.
Performance benchmarks on H100 GPUs, throughput/latency analysis, concurrency scaling, and decision matrix for choosing the right serving engine for your workload.
GPU memory requirements for Llama 3 models, quality trade-offs between full fine-tuning and LoRA/QLoRA, cost analysis, and production deployment code examples.
Connect any LLM to any server, build custom agents that browse the web, search Airbnb, control Blender. Complete guide with code examples and production patterns.
Revolutionary approach - encode text chunks as QR codes in video frames, achieve 50-100× compression vs vector databases, retrieve in <100ms with constant 500MB RAM.
Hybrid search (keyword + vector), reranking, query expansion, chunking strategies, evaluation harness, and guardrails to reduce hallucinations in production.
Quality/speed/memory trade-offs, how quantization affects KV cache and throughput, and a practical decision matrix for on-prem serving.
Metrics (TTFT/TBT, p95, cost), traces (OpenTelemetry), audit logs, evaluation telemetry, and alert rules for enterprise-grade LLM operations.