AIGP Quest · cheat sheet · BoK v2.1

I.C Policies across the life cycle

I.C Policies across the life cycle (6 to 8 exam questions)

Establish policies and procedures to apply throughout the AI life cycle.

I.C.1 · Policy from day zero: plan, assess, design

Policies should cover every life-cycle stage, from use-case assessment to incident management.
Intake triages every AI proposal into a risk tier and records approved uses.
Risk policy sets the method, the tolerance and who may accept residual risk.
Ethics by design starts at planning; data rules cover rights, provenance, quality, minimization and retention.

Use-case assessment:: The intake review that captures an AI proposal's purpose, owner, data and affected people, and triages it by risk.
Risk tier:: A category (such as prohibited, high, medium or low) that sets how much review and control an AI use receives.
Residual risk:: The risk that remains after mitigations, which someone with the right authority must formally accept.
Ethics by design:: Identifying and resolving ethical issues from planning and design onward, so values shape the system rather than being checked at the end.
Data provenance:: A documented record of where data came from and how it has been transformed.

Exam tips: Planning order: business problem → use case → applicable laws → risks and gaps → data. Data-first or development-first options are wrong. · Ethics by design means from the design phase onward. A pre-launch ethics gate, even one with veto power, is the near-miss. · Laws attach to a defined use. 'Map all AI laws' before the use case exists is a tempting but premature answer.

I.C.2 · Policies from build to breakdown

Development: document, secure and vet components. Testing: separate data, criteria first, adversarial testing.
A readiness review gates deployment; approved uses and monitoring keep the system on track.
The inventory covers every system; a model card summarizes one model; decision records limit misuse and liability.
Incidents: detect, triage, contain, fix, learn, with notification running alongside.

Readiness review:: The go/no-go check before deployment that confirms testing, documentation, oversight, training and incident plans are in place.
Scope creep:: An AI system gradually being used for purposes beyond those it was assessed and approved for.
AI inventory:: An organization-wide register of AI systems with their owners, purposes, risk tiers and status.
Model card:: A concise document summarizing one model's purpose, intended use, performance and limitations.
Red teaming:: Deliberately attacking your own AI system, as an adversary would, to find weaknesses before release.
AI incident:: An event where an AI system's development, use or malfunction causes or nearly causes harm.

Exam tips: A policy that stops at deployment approval is incomplete. The IAPP answer adds monitoring, incident management, documentation and reporting. · Adversarial testing before launch is responsible. Options calling it unethical, or deferring it to production, are inversion traps. · An approved system used for a new purpose = scope creep. The fix is documented approved uses plus mandatory re-review, not accuracy monitoring. · Deactivation triggers are regulatory or performance problems, never profit and never 'a new version exists'.

I.C.3 · Retrofit, don't reinvent: updating policies

Evaluate and update existing policies for AI rather than writing a parallel rulebook.
Privacy, security, data governance and IP each need AI-specific additions.
Out-of-cycle review triggers: AI into an existing program, a new AI program, a new regulation.
Inferences can be personal data, and AI outputs aren't automatically owned by the prompter.

Gap analysis:: Reviewing existing policies against AI's characteristics to find what they fail to address.
Out-of-cycle review:: An immediate policy review triggered by AI entering an existing program, a new AI program, or a new regulation.
Prompt injection:: An attack that hides malicious instructions in a model's input to hijack its behavior.
Data poisoning:: An attack that corrupts training data so the model learns harmful or wrong behavior.
Data lineage:: The traceable path of data from its source through every transformation into a model.

Exam tips: Three out-of-cycle triggers: AI into an existing program, a new AI program, a new regulation. Marginal changes ride the normal cadence. On LEAST/NOT questions, pick the marginal change. · The BoK's four examples of policies to update are data privacy, security, data governance and intellectual property. Expect them as options. · Training on content you don't have rights to is an IP problem first. Security is the tempting near-miss.

I.C.4 · Buying AI: procurement and supply chain

Third-party AI follows a life cycle: identify, assess, contract, onboard, monitor, exit.
Due diligence asks about training data rights, testing, documentation, data use, updates and sub-suppliers.
Contracts need AI-specific clauses, not just commercial boilerplate.
Map the supply chain behind the vendor: foundation models, data suppliers, open-source components.

Third-party AI risk:: Risk from AI systems, models, data or components supplied by vendors and their own suppliers.
Vendor due diligence:: Risk-scaled investigation of a supplier's AI: training data, testing, documentation, security, data use and update practices.
Change-notification clause:: A contract term requiring the vendor to tell you before material model updates or new AI features go live.
Fourth party:: Your vendor's own supplier, such as the company whose foundation model the vendor builds on.
AI bill of materials:: An inventory of the models, datasets and components that make up an AI product.

Exam tips: 'We couldn't tell which users were affected' → the answer is a third-party AI inventory plus documented decisions. Encryption or incident playbooks are near-misses. · The AI-specific clause set covers data-use limits, output ownership, change notification, testing evidence, incident cooperation and audit rights. Confidentiality and uptime are generic. · You can outsource the tool, never the accountability. 'The vendor is responsible' is a trap answer.

I.C.5 · People rules: HR and acceptable use of AI

Third-party risk also enters through people: staff using outside AI tools, and vendors' AI used on staff.
The AUP binds people; contracts bind vendors; HR policies govern AI decisions about workers.
A good AUP covers approved tools, prohibited inputs and uses, human review, disclosure, accounts, reporting and consequences.
AI in employment decisions needs assessment, notice, human review, a way to contest, and vendor flow-down.

Acceptable use policy (AUP):: Rules binding employees and others on how they may use AI tools: approved tools, prohibited inputs and uses, review and reporting.
Shadow AI:: Employees using AI tools the organization hasn't approved or assessed.
Prohibited inputs:: Categories of data, such as confidential, personal or regulated information, that must never be entered into external AI tools.
Meaningful human review:: Review by a person with the competence, information and authority to change an AI-informed decision.

Exam tips: Employee conduct with unsanctioned tools → acceptable use policy. Procurement clauses are the near-miss when no contract exists. · An AUP clause claiming approved tools are 'error-free' or need 'no review' is always a trap. · Human editing of individual outputs doesn't cure systematic bias. 'A human reviews it' is a cure-all trap. · In employment, consent demanded as a condition of the job is a weak basis. Look for assessment, notice and human review instead.

Independent study aid, not affiliated with the IAPP. Written with AI from primary sources; laws change, so check the sources. Last updated 7 Nov 2026.