Global AI Data Security Guidelines Released The NSA’s Artificial Intelligence Security Center and CISA published joint guidance emphasizing data integrity for AI systems. Key recommendations include using digital signatures to authenticate data, tracking data provenance, and securing infrastructure. The report warns of risks like malicious data manipulation and data drift, urging organizations to adopt monitoring tools and access controls. These practices aim to protect sensitive information in defense, healthcare, and critical infrastructure sectors.

Meta’s GDPR-Compliant AI Training in the EU Ireland’s Data Protection Commission (DPC) approved Meta’s plan to train AI models on public social media data starting May 27. To address privacy concerns, Meta introduced enhanced user controls, including an in-app objection form and options to make posts private. Critics like privacy group noyb argue the policy violates GDPR principles, threatening legal action. The DPC emphasized users must actively adjust privacy settings to opt out, raising questions about default consent in AI development.

Balancing AI Innovation and Privacy Risks A Dark Reading analysis noted AI’s potential to strengthen security through automated threat detection and policy enforcement. However, poorly managed systems risk amplifying biases or exposing sensitive datasets. Experts recommend regular audits and de-identification techniques to mitigate harm. As governments debate regulations, companies face pressure to prioritize ethical AI governance alongside technological advancements.

Emerging Challenges and Solutions The Irish DPC and U.S. agencies both highlighted the need for transparent AI workflows and user education. While Meta’s case shows progress in regulatory collaboration, gaps remain in addressing cross-border data flows and real-time monitoring. Proactive measures like output filtering and risk assessments are now seen as critical to maintaining public trust in AI systems.