Split-view poisoning
A data poisoning technique that exploits datasets storing references (typically URLs) to content rather than the content itself; an attacker gains control of the resource a reference points to, most commonly by purchasing an expired domain, so that anyone resolving the reference after that point receives attacker-controlled content, labeled however the original dataset entry specified.
Defined in 2 GAGE programs, which carry 2 distinct definitions of it. The wording above is taught in Certified AI Data Governance Professional (CADGP).
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
A data poisoning technique that exploits datasets storing references (typically URLs) to content rather than the content itself; an attacker gains control of the resource a reference points to, most commonly by purchasing an expired domain, so that anyone resolving the reference after that point receives attacker-controlled content, labeled however the original dataset entry specified.
A demonstrated attack (Carlini et al., 2023) on datasets that store links rather than content: the attacker buys expired domains that appear in the dataset and serves different content to the dataset's crawler than the original annotator saw, so a client's downloaded data differs from what was documented.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- The poisoned quarter: an attacker has been feeding your pipelines, find the entry point · Adversarial Data Governance, Certified AI Data Governance Professional (CADGP)
- Auditing your own data: provenance, gaps, and quiet poison · Data Reality, Certified AI Governance Professional (CAIGP)
Where it shows up at work
A definition is where Split-view poisoning starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Profiles data, sets rules and thresholds, monitors, assigns issues to owners, and knows when data is not fit to train or run a model.
One lesson on this page teach it as a primary outcome.
Traces where data came from, what transformed it, who owns each hop and where it flows downstream, so a number can be defended.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- Chief Data Officer, data leadership editionneeds it at depth
- Data Governance Leadneeds it at depth · 2 open postings this week
- AI Model Validatorneeds working knowledge
- AI Privacy Engineerneeds it at depth · 6 open postings this week
- Privacy Analystneeds working knowledge
- AI Auditorneeds working knowledge · 9 open postings this week
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: EU AI Act Article 10; ISO 8000; NIST AI RMF MAP 4.
One interview question about it
Which data quality rules would you monitor for a model in production, and what happens when one fails?
Asked of candidates for data quality rules and monitoring. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Data analysis, stewardship and business intelligence carries it as work you can show: Quality rules and profiling results are your artifacts.
- A data science, statistics or analytics degree carries it as a claim to back up: Data quality rules are familiar; monitoring them in production is new.
- Privacy and data protection carries it as work you can show: Data maps and records of processing are lineage by another name.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.