The AI you didn't build: From black box to defensible risk
Contributors:
Darren Abernethy
AIGP, CIPP/A, CIPP/C, CIPP/E, CIPP/G, CIPP/US, CIPM, CIPT, FIP, PLS
Shareholder
Greenberg Traurig, LLP
Joanne Furtsch
CIPP/C, CIPP/US, CIPT, FIP
VP, Privacy Knowledge
TrustArc
Hilary Wandall
AIGP, CIPP/E, CIPP/US, CIPM, FIP
Chief Ethics and Compliance Officer
Dun & Bradstreet
Brought to you by TrustArc
Broadcast date: 24 Sept. 2026
Time: 08:00–09:00 PDT, 11:00–12:00 EDT, 17:00–18:00 CEST
Most AI risk today isn't homegrown, it's embedded in the third-party software you already use. When privacy and risk teams can't inspect the model, "high risk" isn't a defensible answer.
A practical framework is needed for discovering AI hidden in vendor products, mapping it to data and business processes, and scoring risk in a way that holds up to leadership, auditors, or regulators. The outcome: separating inherent from residual risk, translating findings into procurement and contract terms, and building the evidence trail to answer, "How did you arrive at that number?"
Key takeaways:
- How to identify AI embedded in third-party products, even when vendor documentation is incomplete.
- A repeatable structure for scoring third-party AI risk using data sensitivity, purpose, autonomy, and consequence.
- How to distinguish inherent risk from residual risk, without treating vendor assurances as mitigation by default.
- How to translate a risk assessment into contract terms, monitoring obligations, and escalation triggers.
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Eligible CPEs: AIGP, CIPP/A, CIPP/C, CIPP/CN, CIPP/E, CIPP/US, CIPM and CIPT.
1.0 CPE credits
