01 / Readiness
AI infrastructure readiness
Assess compute, network, storage, latency, capacity, serving, isolation, and operational support requirements.
AI.05 // AI & Operational Intelligence
AI & OperationalIntelligence.
Deploy intelligence without weakening trust.
We engineer the data pathways, infrastructure, security boundaries, evaluations, and approval controls needed to apply AI to infrastructure and security operations while preserving provenance and operator authority.
EVIDENCE-TO-ACTION FUNNELSIMULATED // OPERATOR CONTROL
Evidence-groundedUse-case drivenHuman authorizedContinuously evaluated
AI.05 // CAPABILITY OVERVIEW
From operational signal to bounded decision support.
AI is useful in operations when it helps people interpret evidence, exposes uncertainty, and remains inside a clearly defined authorization boundary.
We focus on infrastructure readiness, governed data, evaluation, and narrowly scoped integration—not generic chatbot claims or unattended production authority.
02 / Data
Operational data pathways
Prepare logs, metrics, traces, flows, configurations, tickets, and approved knowledge with lineage and access policy.
03 / Assist
AI-assisted operations
Support anomaly investigation, change analysis, configuration reasoning, and operator decision-making under uncertainty.
04 / Ground
Evidence-grounded knowledge
Retrieve approved architecture, policy, configuration, and runbook material with visible source attribution.
05 / Control
Tool-controlled action
Connect models to narrowly scoped tools with explicit permissions, structured contracts, dry runs, and human approval.
06 / Govern
Evaluation & lifecycle governance
Measure grounding, failure behavior, data handling, latency, cost, drift, and operator feedback over time.
AI.05 // ENGINEERING FIELDS
Intelligence designed around authority and evidence.
Each field addresses what must be true before an AI-assisted capability belongs inside a sensitive operational environment.
Determine whether compute, network, storage, isolation, performance, and support models fit the intended use case.
Engineering focus
Accelerators, inference serving, latency, capacity, east-west traffic, storage, resilience, observability, and lifecycle.
AI.05 // TECHNOLOGIES & METHODS
Infrastructure, data, models, and controls as one system.
The architecture begins with the use case and data boundary. Model choice is one decision among serving, access, retrieval, evaluation, observability, and operator control.
01 / AI infrastructure
AI infrastructure
Provide controlled compute, transport, storage, and serving.
// Accelerators// High-throughput networking// Containers// Inference endpoints// Storage// Isolation
02 / Operational data
Operational data
Prepare governed signals and knowledge for reliable use.
// Telemetry pipelines// Event streams// Relational retrieval// Vector retrieval// Knowledge stores// Lineage
03 / Model integration
Model integration
Connect hosted or self-managed models through explicit contracts.
// Model endpoints// Retrieval// Structured outputs// Tool interfaces// Prompt/version control
04 / Operations interfaces
Operations interfaces
Bring evidence from systems operators already trust.
// Monitoring// SIEM// Ticketing// CMDB/source of truth// Automation controllers
05 / Trust controls
Trust controls
Protect data and keep actions attributable.
// Access policy// Classification// Redaction// Secrets// Provenance// Audit records
06 / Evaluation
Evaluation
Measure useful behavior and failure before operational use.
// Grounding tests// Safety cases// Scenario suites// Human review// Drift monitoring// NIST AI RMF guidance
Model and service selection must match the customer’s data classification, authorization boundary, deployment requirements, and evaluation results. No autonomous production authority is implied.
AI.05 // SIGNAL TRIAGE
Evidence before recommendation. Authority before action.
Operational signals become an inspectable hypothesis with cited supporting evidence and a visible operator decision boundary.
SIMULATION ONLY // NO LIVE SYSTEM CONNECTION
Begin with an observable condition.
Receive a defined event or anomaly and preserve its source, time, scope, and confidence context.
Decision-support trace01 / 05
signal / packet loss after change time / 14:32Z scope / branch-04 source / telemetry alert
Signal preserved.
AI.05 // DELIVERY APPROACH
Select the use case. Bound the risk. Evaluate the system.
AI-assisted capability enters operations only after the data, behavior, permissions, failure conditions, and human responsibilities are understood and tested.
Choose a bounded use case.
Define the operator, decision, evidence, expected value, unacceptable behavior, and data boundary.
Examine data, infrastructure, and risk.
Map sources, lineage, access, deployment constraints, threat paths, serving needs, and evaluation criteria.
Test in isolation.
Build a controlled prototype, exercise normal and failure scenarios, measure grounding, and document limitations.
Add guardrails and transfer ownership.
Apply scoped permissions, approval, observability, runbooks, feedback, change control, and retirement criteria.
REPRESENTATIVE DELIVERABLES
Use-case and risk matrix · Reference architecture · Data lineage · Threat model · Evaluation suite · Tool-permission design · Guardrail policy · Operational runbook · Monitoring plan
AI.05 // START A CONVERSATION