Industry challenge
Telecom AI assurance
Explore HAIEC's telecom assurance narrative, including AI agent behavior, operational evidence and source-linked challenge work.
Explore telecomExplore the HAIEC platform
Know what AI can cause. Prove what the evidence supports.
AI agents can call tools, access sensitive systems and change real-world outcomes. HAIEC connects consequential actions with authority, capabilities, controls and qualified evidence, so teams can make accountable decisions without relying on model-generated assurance.
Deterministic AI Action AssuranceEvidence-Bound AssuranceHuman accountability
HAIEC / ACTION LANDSCAPE
Understand → AssureGovern & operate with qualified evidence
Real sample assurance report
Six selectable capabilities
Interactive learning labs
No-card 14-day SCAN trial
01 / Start with your objective
What do you need to establish?
Start from a question, not a prerequisite checklist. HAIEC supports several independently selectable jobs. Choose the objective that matters to your team.
Selected path / Discovery
See what your AI is capable of reaching.
Connect an eligible source, identify supported AI systems and follow agent-to-tool-to-target relationships. Understand technical exposure before higher assurance claims are made.
Where this leads
Agent & Action Map, AI inventory, source-backed findings
Explore AI Inventory 02 / Interactive action walkthrough
Trace an action. Examine the evidence.
Follow one example of an AI agent invoking a sensitive business operation. See how technically possible, authorized and observed mean different things.
Educational example, not a live evaluation
Use the guided steps or inspect any question directly.
EXAMPLE / REFUND AGENTSTEP 01 OF 04
A reachable operation is not an approved decision.
An AI assistant can call a refund service capable of changing a payment destination. First, establish the action path itself.
What is established
The inspected code path can reach a sensitive operation.The path is meaningful even without runtime telemetry. A reachable handler alone does not prove a refund occurred.
What HAIEC adds
A consequence-first technical finding.Connect the agent, tool and downstream operation rather than presenting disconnected scan alerts.
Explore the Action & Access Map ↗ILLUSTRATIVE WALKTHROUGH
Five independent evidence questions
Select a plane to inspect what it can establish.Requested: the initiating intent
Explore DAI ↗The instruction or workflow request describes the desired action. It does not itself grant business authority, prove effective access or establish execution.
03 / The platform
Six capabilities.
One evidence foundation.
Discover, Evaluate, Authorize, Monitor, Protect and Verify are independent customer capabilities, not a mandatory assembly line. Select one to see its inputs, value and potential outputs.
Understand
What exists and what could happenAgents, systems, tools, identities and consequential paths.
Assure
What the evidence establishesQualified evaluations, authority comparison and control proof.
Govern & Operate
What to decide and how to respondBoundaries, decisions, connected monitoring and verified controls.
Selected capability
Discover what AI can cause.
See systems, agents, tools, identities and supported action paths. Find source-backed exposure before runtime or policy evidence is available.
TYPICAL INPUTAuthorized repository, system metadata, tool definitionsPRACTICAL RESULTAgent & Action Map, initial exposure findings and coverage gapsNeed the full technical architecture rather than this guided overview?
Open the Enterprise Architecture Explorer04 / Evidence over assertion
See the proof structure
before the promise.
Qualified sources, explicit unknowns and exact report boundaries are central to the product. Explore how HAIEC exposes what is supported without treating absent evidence as success.
Operational demonstration
Manufacturing assurance
See the Station 217 scenario and the connection between agent actions, ERP/MES changes, business consequences and human review.
Explore manufacturingEvidence portfolio
Reports, maps and receipts
Compare real-repository, current generated and demonstration artifacts, each with stated evidence scope and provenance.
View sample reports05 / Learn by doing
Understand the method.
Practice the decisions.
Use HAIEC's interactive labs and workbooks to learn action assurance, controls, incident response and evidence interpretation. Public educational content can be explored without starting a trial.
Self-paced course01
AI Assurance Crash Course
A self-paced introduction to consequential AI, evidence boundaries and assurance decisions.
Start the courseInteractive lab02
DAI Authority Decision Lab
When does an AI agent have permission, and when does a consequential choice need its own delegated authority?
Enter the labPractitioner workbook03
45 Assurance Controls
Inspect control families, required evidence and the difference between cataloging, testing and verified operation.
Open workbookScenario atlas04
Attack-to-Consequence Atlas
Follow prompt injection, tool misuse and other security patterns to their potential business consequences.
Explore scenariosResponse simulation05
Incident Response Tabletop
Work through incident signals, ownership, review, remediation evidence and verification decisions.
Begin tabletopIntegration field guide06
Integration & Evidence Guide
Choose the right evidence source for each assurance question, from source analysis to connected telemetry.
Open field guideAdoption playbook07
Enterprise First 30 Days
Follow a practical planning workbook for scope, evidence ownership, technical onboarding and decision review.
Open workbookDecision workshop08
Before You Deploy an AI Agent
An executive decision workshop on consequence, authority and evidence readiness before deployment.
Enter the workshopFree learning hub09
Foundations, Guides & Tools
Explore introductory material, developer references, regulatory guides and practical learning resources.
Explore the libraryLearning is not the same as an assurance evaluation. Practice with educational scenarios, then inspect a real HAIEC report or run a scoped assessment against an eligible system.
See real sample evidence06 / Beyond the platform UI
An ecosystem built for
practitioners and enterprises.
HAIEC combines its assurance platform with developer integration paths, openly available security utilities, research and methodologies. Each supports a different part of the adoption journey.
Build with HAIEC
Connect through the agent-native MCP interface, authorized source integrations or HAIEC APIs. Use existing engineering workflows.
Open-source tools
Explore standalone utilities for AI application security, MCP isolation and local language-model verification. Tool findings and platform assurance conclusions remain distinct.
Research & frameworks
Explore ISAF v2, deterministic assurance methodology, delegated action integrity and published research resources.
One platform. Multiple entry points. Consistent evidence discipline.
View platform overview Product evaluations, research methods, open-source checks and professional engagements have distinct scopes. Explore the layer that fits your objective.
07 / Choose your next move
From exploration
to evidence.
Begin with learning, a self-service SCAN trial or a focused enterprise assurance engagement. The next step should match your system's consequence and evidence complexity.
Explore free
Learn first.
Study the methods, practice consequential-action scenarios and inspect published samples at your own pace.
Public learning content · No payment required
Browse free learning Self-service
Try HAIEC SCAN.
Start scanning eligible AI source surfaces and building your first inventory and evidence baseline.
14-day free SCAN trial · No credit card required at signup
Start 14 days free Enterprise / Priority engagement
Assure what matters most.
Scope one consequential AI system, the authority question and an evidence boundary. Review technical findings and a decision-ready package with the HAIEC team.
Founder-led Enterprise Assurance POC · Scoped engagement
Discuss enterprise assurance Prefer to operate HAIEC through your agent or IDE?
Start with agent integration The MCP interface and operational playbook let your AI agent drive the assurance workflow directly, with the same evidence discipline.