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AI governance for the public sector, run as a program

Algorithmic Impact Assessments scored, tiered, and reviewed on a real assessment engine; an AI systems register your leadership can read; and every AI decision — including the platform's own — on an auditable trail.

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Public-sector AI governance stopped being optional the moment the mandates arrived: Canada's Directive on Automated Decision-Making requires Algorithmic Impact Assessments for federal automated decision systems, and Ontario's Bill 194 brought the first provincial public-sector AI rules alongside its mandatory PIAs. The practical question for institutions isn't whether to govern AI — it's where that governance will live, and whether it will survive an audit.

AccessPoint's answer is to run AI governance on the same machinery that already governs privacy. The assessment engine that runs your PIAs runs your AIAs — screeners, typed questionnaires, impact scoring and tiering, section assignment to the experts who actually understand the system, an embedded risk register, and review sign-off with a full trail. Each assessed system is a durable privacy subject carrying its automated-decision profile, its assessments, its risks, and its incidents over time, and an AI Systems Register report rolls the whole estate up with deployment status and risk classifications.

There's also a second layer most vendors skip: governing the AI inside your own tooling. AccessPoint's optional AI Assist is built responsible-by-design — it runs on Azure OpenAI inside your own tenant, every output is a suggestion a person decides on, prompts and responses are never stored, a token budget you control caps spend, each case's activity feed discloses the AI calls made on it, and case audit exports disclose AI involvement. Your AI governance program and your AI-assisted casework meet the same standard.

What public-sector AI governance software needs to do

The mandates differ by jurisdiction, but the working requirements converge on the same list.

AIAs as real assessments Impact scoring, tiering, expert input by section, and review sign-off — not a fillable PDF that ends its life in a shared drive.

A register of AI systems Which systems make or assist decisions, their risk classification, their deployment status, and their approval position — answerable in one report, not a hunt.

Risks that outlive the assessment Algorithmic risks scored and tracked in a register with treatments and owners, rolling up beside privacy risk — because leadership needs one exposure picture.

Reassessment when systems change AI systems evolve; governance has to recur. Durable subject records and review workflows mean assessment N+1 starts from history, not from blank.

An audit trail regulators can follow Who assessed, who approved, what changed, and when — on a ledger, not in email.

Governance of your own AI tooling If your case-processing software uses AI, that AI needs the same discipline: human decisions, disclosure, spend controls, and data that never leaves your boundary.

How AccessPoint governs AI

One engine for the assessments, one register for the estate, one ledger under all of it.

AIAs on the PIA engine

Assess automated decision-making systems against the directive or framework that applies to you — public-sector AI directives, your own responsible-AI policy, or both — scored, tiered, reviewed, and published with an auditable trail.

AI systems register

ADM-flagged subjects carry their automated-decision profile, and the AI Systems Register report lists the estate with deployment status, risk classification, and the derived approval position.

Experts answer their part

Section assignment sends technical questionnaire sections to the people who built or operate the system; answers autosave and merge on coordinator approval.

Algorithmic risk, managed

Risks from AIAs land in the ISO 31000 register with inherent and residual scoring, treatments, KRIs, and governed acceptance that expires — beside the privacy risks they belong with.

Responsible by design, itself

AccessPoint's own AI Assist runs in your Azure tenant: person-decided outputs, no prompt storage, per-feature switches, a monthly token budget, your choice of inference data zone, and per-case AI disclosure.

Reporting for the oversight you face

Assessment throughput, the AI systems estate, and outstanding commitments — reportable from the same data, with Report Studio for the questions specific to your governance framework.

The mandate wave

AIAs went from best practice to law — and more is coming

The federal Directive on Automated Decision-Making made Algorithmic Impact Assessments mandatory for government ADM systems; Ontario's Bill 194 added provincial public-sector AI rules. Institutions that stand up AIA capacity on governed infrastructure now won't be improvising when the next obligation lands.

Directive-configurable Assessment templates configure to the directive or policy that binds you — and to your own framework where none does yet.

One platform, every obligation AIAs live beside the PIAs, breach program, and access work the same office runs — one system to govern and one ledger to audit.

Assessed in your own tenant Assessments of your most consequential systems stay inside your Microsoft 365 and Azure environment.

Go deeper

AI Governance & AIA Software Questions

What is algorithmic impact assessment software?

Software that runs AIAs as a governed workflow: screening which systems need assessment, scoring impact and assigning tiers under the applicable directive, collecting expert input by section, registering algorithmic risks with treatments and owners, managing review and sign-off, and keeping an auditable record — plus a register view of every assessed AI system. AccessPoint does this on the same engine that runs privacy impact assessments.

Does AccessPoint support the TBS Directive on Automated Decision-Making?

Yes — the assessment engine runs Algorithmic Impact Assessments against the directive or policy that applies to you, with impact scoring and tiering, expert section assignment, an embedded risk register, and review sign-off, all on an auditable trail. Federal institutions run it beside their ATIP processing on the same platform.

How do we keep track of all the AI systems across our organization?

Each assessed system is a durable privacy subject carrying its automated-decision profile, assessment history, risks, and incidents. The AI Systems Register report rolls the estate up — deployment status, risk classification badges, and the derived approval position per system — so 'what AI are we running and on what authority' has a one-report answer.

Our privacy team owns AI governance. Is that the right home?

It's the most common home, and there's a reason: AIAs share their machinery with PIAs — screening, questionnaires, risk scoring, review — and the systems being assessed usually process personal information anyway. Running both on one platform means one methodology, one register, one audit ledger, and no boundary disputes between parallel tools.

AccessPoint itself uses AI. How is that governed?

By construction. AI Assist is optional and deploys into your own tenant's Azure subscription — Azure OpenAI with managed-identity access, prompts and responses never stored, nothing training outside models. Every output is a suggestion a person decides on; each feature switches individually; a monthly token budget you control caps spend; you choose where inference is processed; and every case's activity feed and audit export disclose the AI calls made on it.

Govern AI Before It Governs Your Audit Findings.

Try AccessPoint free for 30 days in your own Microsoft 365 tenant — AIA templates configured by your jurisdiction pack.

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