Learning objectives
By the end you can:
- (Understanding, gated) Apply the full Primary-Source Guardrail (Trace, Match, Recompute, Date-check, Cite) as a domain-general verified pass, adapting what counts as "the source" and "the recompute" across six analyst work types: research, financial modeling, memo drafting, deck building, coding, and data analysis.
- (Understanding (gated) State the professional-accountability principle of AI authorship) the human who submits a deliverable is fully accountable for its correctness regardless of what tool drafted it, and explain why "the AI made the error" is never a valid defense.
- (Understanding, gated) Distinguish legitimate AI-assisted drafting from professional-integrity failures (unverified reliance, presenting ungoverned AI output as fully vetted original work), and name the concrete intellectual-property risks of pasting proprietary firm or client material, or copyrighted content, into a third-party AI tool.
- (Understanding (gated) Decide when and how to disclose AI use in a work product) to a manager, a client, or a regulator, using a concrete trigger-based framework, and explain why appropriately-verified, silent AI-assisted drafting differs from concealment of unverified output.
- (Understanding, gated) Explain the core roles and duties under India's Digital Personal Data Protection Act, 2023 (Data Fiduciary, Data Processor, Data Principal; lawful basis; breach and erasure duties; cross-border transfer; Significant Data Fiduciary; children's data) well enough to correctly diagnose a DPDPA-relevant paste decision.
- (Understanding (gated) Explain the US privacy and confidentiality patchwork relevant to an analyst) state comprehensive privacy laws, the GLBA Safeguards Rule, HIPAA, FCRA, and, separately, why material non-public information (MNPI) and Regulation FD are a securities-law risk category distinct from privacy law entirely.
- (Understanding, gated) Apply a concrete may-paste / may-not-paste field guide to a real analyst scenario in both an Indian and a US context, correctly separating personal-data risk from confidentiality/IP risk from MNPI/insider-trading risk.
- (Understanding (gated) Design your own lightweight, personally-verified AI workflow for a recurring piece of work) naming the tool category, the prompt pattern, the specific verification step, and the by-hand skill it never replaces.
- (Productivity, tool awareness) For any of the six domains, select which category of AI tool to reach for and what its data-handling and verification profile is, cross-referencing the program's other galaxies for the deep by-hand skill each domain rests on.
- (Understanding (gated) Complete a guided run-in-your-own-tool project end to end) brief, independent AI-assisted attempt, and a deterministic verification worksheet graded only against a bundled primary source, producing a verification log a reviewer could act on.
The duality, stated once more (R10). Every module in this branch carries both an understanding objective the gate rewards and a productivity objective that is simply the payoff you keep. Here the split is starker than usual, because this whole node sits at the seam between the two: objectives 1–8 and 10 are what the ≥85% gate tests, the generalized verification discipline, the professional and legal judgment, and the capstone artifact, all things a tool cannot do for you. Objective 9, knowing which tool category to reach for in a given domain, is the awareness you carry day to day, and it is real and valuable, but it never substitutes for the trace-and-recompute that objectives 1 and 10 demand. A learner who can name every AI tool category perfectly and skips the verified pass has not passed this node in any sense that matters.