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Helpful Places

Government of Canada – DTPR AI Register

AI Assistant for IT Support and Service Requests

Inform

What it collects

Operational data
Anonymized data
  • Enterprise service management knowledge base articles containing IT support procedures, FAQs, and guidance — no personal information is described as part of this input source.
Run by
Agriculture and Agri-Food Canada (AAFC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Inform

Milo 2.0 is a conversational AI chatbot embedded in Agriculture and Agri-Food Canada's enterprise IT service management platform. It helps Government of Canada employees get answers to common IT questions and submit IT support tickets without speaking to a technician. The system uses a large language model (LLM) and a knowledge base of IT articles to respond in natural language. Users are informed that they are interacting with an AI system.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Enterprise service management knowledge base articles containing IT support procedures, FAQs, and guidance — no personal information is described as part of this input source.

Processing

Language Models
  • Large language model (LLM) technology embedded within the BMC Helix platform, enabling natural-language understanding and conversational response generation for IT service management queries.
Search & Retrieval
  • Retrieves relevant knowledge base articles to ground chatbot responses, functioning as a retrieval component that feeds documented IT procedures and FAQs into the language model.

What it does

Creating (Generative AI)
Human decides
  • Generates natural-language responses to IT support questions and guides users through service request workflows. Outputs are advisory; the user decides whether to accept the guidance or escalate to a human technician.
Understanding (Semantic AI)
Human decides
  • Retrieves and matches relevant knowledge base articles to the user's question, grounding responses in existing IT support documentation before generating an answer.

Outputs

Generated content
Anonymized data
  • Natural-language answers to IT support questions and guided prompts for completing IT service requests, generated by the LLM based on knowledge base content. No personal information is included in the described outputs.
Operational data
Anonymized data
  • IT support tickets created or pre-populated in the BMC Helix ticketing system as a result of user interactions, routed to the appropriate IT team for resolution.

Run by

Agriculture and Agri-Food Canada (AAFC)
  • Federal department that develops and deploys Milo 2.0 as part of its enterprise IT service management platform for Government of Canada employees.

GC AI Register — Milo 2.0

Built by

BMC Software
  • Technology vendor that provides the BMC Helix enterprise service management platform within which Milo 2.0 is delivered.

GC AI Register — Milo 2.0

Kept for

Not stated by the Helpful Places.

Shared with

Not available to me
  • The register states the system does not involve personal information. No mechanism for individual data access is described, consistent with the system's non-personal-information classification.
Available to the accountable organization
  • IT ticket data and interaction logs generated through the system are accessible to Agriculture and Agri-Food Canada's IT service management function via the BMC Helix platform.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Be Informed of AI UseUsers are informed that they are interacting with an AI system. The register confirms AI use is disclosed to users (field: 'AI use disclosed to users: Y').
  • Right to Algorithmic TransparencyThe Government of Canada AI Register entry for Milo 2.0 is publicly available, providing general information about how the system works, its data sources, and its purpose. Employees seeking further detail may consult the published register entry.

Risks and safeguards

  • Psychological harmLLM-generated responses may be inaccurate or misleading, potentially causing employee frustration or loss of confidence in IT support services.Safeguard: The system is grounded in a curated enterprise knowledge base to reduce hallucination; users can escalate to human IT staff at any time; AI use is disclosed to users so they understand they are interacting with an automated system.
  • Societal & cultural harmReliance on a vendor-supplied LLM for internal government IT support may introduce dependency on proprietary technology and reduce transparency into how government services are delivered.Safeguard: The system is described as developed in part by AAFC, and the knowledge base used is an internal enterprise resource; the register entry is publicly disclosed, supporting accountability.