Market
AI law in CA-ON
1 binding provision TwinEthos encodes that reach CA-ON: 1 in force, 0 enacted but not yet applying. Start from the guards to add.
Informational data, not legal advice. Summaries and rules have not been reviewed by a lawyer: always verify official law text for decisions. A suggested guard is intended to address each rule; adding it is not a statement of compliance to that law.
The guards that cover the most here
40 guards address 54 items across 1 jurisdiction with binding law: 1 binding law in force, 0 enacted but not yet applying, 29 standards and frameworks, 24 TwinEthos recommended guardrails.
A public job posting does not say that AI screens, assesses or selects applicants
Render an AI-use statement into every published posting whose applicant pipeline uses AI, and archive each posting as published.
Addresses 1 item: 1 binding law in force
Law in force in CA-ON.
Adverse AI decision without explanation/appeal
Send each adverse AI-assisted decision with its main reasons and the AI's role, plus a way to correct data and appeal to a human who can change the outcome.
Addresses 3 items: 2 standards · 1 recommended guardrail
AI chat interaction without disclosure
Show an AI-identity notice at or before the first assistant turn, in the UI or as the opening message, and answer truthfully when asked if it is a bot.
Addresses 3 items: 2 standards · 1 recommended guardrail
Untrusted content influences instructions or tools
Keep fetched, retrieved, and tool-returned content out of the system prompt, pass it as delimited data, and restrict which tools a turn holding that content can call.
Addresses 3 items: 2 standards · 1 recommended guardrail
Agent actions not traceable to identity and owner
Give each agent its own credential and a registry entry naming an accountable owner, and log every tool action with agent_id, owner, action, target, and timestamp.
Addresses 2 items: 1 standard · 1 recommended guardrail
Agent high-impact action without human approval
Classify agent tools by impact and route every high-impact or irreversible call through an enforced human-approval step in the executor, with the decision logged.
Addresses 2 items: 1 standard · 1 recommended guardrail
AI usage, agent steps, and spend not bounded
Cap agent steps and output tokens on every model call, set per-key and per-project budgets with usage alerts, and issue scoped, expiring model keys.
Addresses 2 items: 1 standard · 1 recommended guardrail
GenAI in consequential decisions without confabulation/output-validation controls
Validate GenAI output against a schema and its cited sources, and send unverifiable claims to review, before it drives a consequential decision or record.
Addresses 2 items: 1 standard · 1 recommended guardrail
GenAI with untracked third-party components (value chain)
Keep an inventory of every third-party model, dataset, package, plugin, and MCP server with pinned versions, its reviewed model card or vendor due-diligence record, and an owner.
Addresses 2 items: 1 standard · 1 recommended guardrail
GenAI output path without PII/sensitive-data leakage detection
Scan generated output for personal data, credentials, and secrets, and redact or block it before it is returned, posted, or sent beyond its authorized audience.
Addresses 2 items: 1 standard · 1 recommended guardrail
The top 10 of 40; the build plan ranks all of them and lets you narrow by AI feature.
By AI feature
Plans for one feature in CA-ON:
Laws
Informational data, not legal advice. Summaries are TwinEthos's own words and rules have not been reviewed by a lawyer: check the official text before relying on any of it. A guard addresses an item; adding it is not a statement that your code meets any law.