Meta's $25B fine underscores risks of data privacy missteps
The fine imposed by a New Mexico judge shows how serious the consequences of misusing customer data can be for companies, as the risks of that grow in the AI era.
A $25 billion fine imposed by a state judge in New Mexico against Meta this week for misleading Facebook users about its data privacy practices is a stark example of the potential consequences of inappropriate data use by companies -- and data professionals and consultants said the risk of privacy missteps is only growing larger as AI deployments increase.
The case in New Mexico also opens a new avenue for privacy-related litigation beyond alleged violations of data protection laws such as GDPR and the California Consumer Privacy Act (CCPA). New Mexico sued Meta in 2021 under the state's consumer protection law, which prohibits unfair and deceptive practices by organizations. The suit stemmed from political consulting firm Cambridge Analytica's 2014 harvesting of the personal data of tens of millions of Facebook users without their knowledge.
Anne Marie Smith, principal consultant at Alabama Yankee Systems, said the business risks of misusing customer data range from reputational damage and loss of customer trust to "substantial" regulatory penalties and possible litigation. She added that beyond the immediate financial consequences, such as the fine Meta faces, data privacy problems can have long-term business impacts that may be difficult to reverse.
AI use raises data privacy stakes
Deploying AI models and agents further increases privacy risks by expanding how customer data is accessed, combined and used -- sometimes in ways that existing data governance controls didn't anticipate, Smith told TechTarget. Potential issues include unauthorized data access by AI tools, exposure of sensitive information and agentic AI systems using customer data to take actions beyond their intended scope.
In a panel discussion during Dataversity's AI Governance Online conference late last month, Anant Somvanshi, a senior specialist in the Data & AI Defense Office at financial services firm Vanguard, said data governance teams previously focused on privacy-related questions such as what data an organization collected about customers and who could access it. Now, he said, they also have to ask things like:
What customer data is being used to train AI models?
Can AI prompts reveal sensitive information?
Can AI models or agents infer information they weren't provided?
If you didn't do your privacy homework when you needed to when GDPR came out and then everything else, you're hosed.
Kelle O'NealFounder and CEO, First San Francisco Partners
Kelle O'Neal, founder and CEO of consulting firm First San Francisco Partners, issued a blunt warning to data governance leaders about the potential privacy fallout of improperly managed AI use. "If you didn't do your privacy homework when you needed to when GDPR came out and then everything else, you're hosed," she said as part of the panel discussion.
O'Neal added that if the right controls aren't in place, a swarm of agents can easily misuse data. "They don't even need to be bad actors in order to create a massive problem," she said. "They're just agents trying to do their job."
Regulatory compliance isn't enough
Smith noted that complying with regulations such as GDPR, CCPA and HIPAA is "essential but insufficient on its own" to prevent privacy violations by both human and AI users. "Regulatory compliance establishes a necessary baseline for protecting personal data," she said -- but it doesn't fully address the privacy and ethical risks associated with data access, analysis and use.
Strong data governance programs also require clear policies on appropriate use of customer data, along with comprehensive classification of sensitive data, suitable access controls "and ongoing monitoring to ensure that data is used responsibly and consistently with its intended purpose, even when a particular action does not explicitly violate a regulation," Smith said.
Craig Stedman is a senior reporter on TechTarget's Data news team, covering analytics, data management and data backup topics. He has more than 40 years of experience as an enterprise IT writer and editor.
