A CISO's Playbook for Defending Data Assets Against AI Scraping frames scraping as a business risk, not just a technical nuisance, and is authored by Areejit Banerjee, Senior Manager of Data Protection Strategy & Product Trust and a researcher in AI governance at Purdue University.
The piece argues that organisations should adopt a strategic mandate that treats scraped data as a corporate asset, aligning the board around risks such as revenue erosion, IP dilution, and infrastructure theft, with measurable success metrics like scraping telemetry and large-scale extraction detection times.
It then advises mapping the scraping risk landscape using an asset-centric inventory, standard threat language from the OWASP Automated Threat ontology, and a data-flow view to distinguish high-value data from commodity content. A two-track response is recommended: a tactical path to tighten defences on top-value endpoints and a strategic path to redesign data exposure, including options like login requirements, API data minimisation, or pricing tiers for human versus automated access.
The guidance also notes that major platforms including Ryanair, LinkedIn, Craigslist and other publishers have already argued in court that scrapers free-ride on their infrastructure and data investments. Overall, the playbook seeks to turn “We’re being scraped” into a measurable, board-ready programme that defends intellectual capital and can even become a competitive advantage.