sub-AI (opt) / VI. TRUSTED, SECURE & MISSION-CRITICAL AI

AI Verification & Validation

Secure AI services for explainability, governance, validation, assurance, privacy, deployment control, and threat operations. Senior AI engineer scope covers policy control, assurance case, red-team workflow, privacy boundary, evidence repository, and release gate, using risk register, protected data samples, compliance controls, abuse cases, access policies, and test evidence and measurable checks for policy violation rate, explainability coverage, leakage risk, attack path exposure, and recovery posture.

Architecture & Risk Blueprint

Senior AI engineering discovery for architecture, risk boundaries, data readiness, and implementation scope.

For AI Verification & Validation, the AI engineer will map target workflows, data owners, integration points, and known failure modes before design starts
Define policy control, assurance case, red-team workflow, privacy boundary, evidence repository, and release gate with clear service boundaries, control points, and engineering assumptions
Prepare the dataset, model, runtime, access, and logging requirements needed for AI Verification & Validation
Build the evaluation plan for policy violation rate, explainability coverage, leakage risk, attack path exposure, and recovery posture so acceptance is measurable, not impression-based
Document risks around unsafe action, data leakage, dependency failure, integration drift, unclear accountability, and evidence gaps and turn them into mitigation tasks with named owners
Deliver architecture diagrams, runbooks, test records, release notes, acceptance criteria, and engineering backlog for procurement, technical review, and implementation approval

USD 2.436.296 IDR 42.026.100.000 Request Scope

Controlled Prototype & Evaluation

A limited working system to test behavior, integration boundaries, evaluation criteria, and operational limits.

For AI Verification & Validation, the AI engineer will implement a controlled prototype around the highest-risk workflow, not a presentation mockup
Connect risk register, protected data samples, compliance controls, abuse cases, access policies, and test evidence to a limited runtime using test credentials, sandbox data, and strict access separation
Build the first safety filter, audit service, privacy guard, monitoring rule set, incident workflow, and evidence export slice with traceable inputs, outputs, errors, and reviewer notes
Run evaluation cases for policy violation rate, explainability coverage, leakage risk, attack path exposure, and recovery posture and record pass, fail, and uncertain outcomes
Review security, data handling, prompt or policy behavior, and integration limits before production planning
Produce a prototype report with architecture changes, blocked items, engineering estimates, and release criteria

USD 3.508.765 IDR 60.526.200.000 Request Scope

Recommended

Production Engineering

Production implementation with service boundaries, pipelines, evaluation harness, security controls, and handover.

For AI Verification & Validation, the AI engineer will build the production service boundary for AI Verification & Validation with repository structure and release workflow
Implement safety filter, audit service, privacy guard, monitoring rule set, incident workflow, and evidence export with environment separation, secrets handling, API contracts, and rollback path
Engineer data preparation, model routing, policy checks, test fixtures, and operational telemetry for the service
Create automated evaluation jobs for policy violation rate, explainability coverage, leakage risk, attack path exposure, and recovery posture before every accepted release
Add audit records, access controls, monitoring operational intelligence interfaces, incident notes, and dependency version tracking
Hand over diagrams, runbooks, acceptance records, deployment notes, and backlog items to the owner team

USD 5.079.786 IDR 87.626.300.000 Request Scope

Operations, Reliability & Governance

Operating model for reliability, monitoring, release control, evidence records, and long-term technical governance.

For AI Verification & Validation, the AI engineer will set up operating controls for release approval, model or policy change, incident response, and evidence storage
Define service indicators for policy violation rate, explainability coverage, leakage risk, attack path exposure, and recovery posture with alert thresholds, review cadence, and escalation roles
Create monitoring for safety filter, audit service, privacy guard, monitoring rule set, incident workflow, and evidence export including latency, errors, drift, cost, version changes, and abnormal behavior
Run tabletop recovery checks for unsafe action, data leakage, dependency failure, integration drift, unclear accountability, and evidence gaps and document the decision path for urgent rollback or shutdown
Prepare governance records for access review, data retention, evaluation evidence, vendor dependency, and stakeholder reporting
Maintain an engineering roadmap for reliability, cost, security, and capability improvements after launch

USD 6.708.777 IDR 115.726.400.000 Request Scope