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Commissioner Advocates Stronger GDPR Safeguards In Tax Reform

Commissioner Maria Christofidou, the authoritative figure in personal data protection, has recently reiterated her support for the government’s ambitious tax reform measures. On both November 14 and November 21, she underscored the need for a balanced approach that safeguards citizens’ privacy while reinforcing the fiscal system.

Addressing Previous Concerns

Earlier, the Commissioner expressed worries about the excessive centralization of power within the Tax Department, a concern detailed in a recent analysis. Her latest recommendations echo these early concerns by emphasizing that any adoption of advanced artificial intelligence systems by the tax authority must be accompanied by robust data protection measures.

Integrating Safety Valves With Artificial Intelligence

The Commissioner is calling for the integration of explicit safety mechanisms within the legislative framework governing tax reform. This provision is critical to ensure that the collection, analysis, and processing of mass data—spanning both public and private sources—remain strictly aligned with the overarching principles of the General Data Protection Regulation (Gdpr). The proposed measures would mitigate risks of bias, discrimination, and potential infringements on individual privacy that could arise from algorithm-driven decision-making.

A Call For Transparent And Effective Governance

In her communication with the Parliamentary Committee on Finance, Christofidou welcomed the government’s initiatives aimed at curbing tax evasion and avoidance. However, she stressed that any legal framework established must be robust, transparent, and capable of balancing effective tax collection with stringent data protection standards. Central to this balance is the necessity for comprehensive data governance and mandatory impact assessments under Articles 35 and 36 of the Gdpr.

Legislative Timetable And Future Implications

The Commissioner further argued that the forthcoming tax reform legislation, particularly the Guarantee and Collection Act, should include a general provision that refers explicitly to the Gdpr. This measure is intended to ensure that all data collection and processing activities conducted by the tax authority are performed within an established regulatory framework that upholds legality, integrity, objectivity, transparency, and proportionality.

Additionally, she recommended that, should the Tax Department employ artificial intelligence systems in its operations, an in-depth data protection impact assessment must be completed. This proactive approach would provide a crucial safeguard both for the tax authority and any entity that contributes data, thereby bolstering confidence in the public administration’s handling of sensitive information.

Imminent Parliamentary Review

Simultaneously, deliberations continue in the Parliamentary Committee on Finance, where six draft bills are under discussion. Owing to time constraints, an emergency session has been scheduled for Thursday. In response to government directives, the legislature is expected to approve the tax reform initiative before year-end to facilitate its implementation by January 1, 2026. It is anticipated that the draft bills will be presented before the full Parliament during the first half of December.

The Commissioner’s remarks underscore the critical intersection of technological innovation and regulatory oversight in contemporary tax administration—a balance that will define the future of both fiscal policy and data privacy.

Meta Unveils Muse Wonder Models In A Bid For Frontier AI Leadership

Meta has introduced two new AI models, Muse Wonder and Muse Wonder Full, signalling a more ambitious move into frontier AI. The releases build on Muse Spark but target more demanding tasks, including coding, while placing greater emphasis on long-term memory and personalised assistance.

Larger Models Target More Demanding Tasks

Unlike Muse Spark, which was designed for low latency and lightweight use, Muse Wonder and Wonder Full are significantly larger and, according to Meta, more capable. The company reports stronger benchmark performance and improvements on demanding workloads, including coding.

According to Meta’s model card, both models achieved benchmark results comparable to Fable 5.1 and GPT-6 Astra in the company’s internal testing. Meta described the release as “the next step on our scaling ladder and a major leap toward personal superintelligence” in its announcement.

Pricing Reflects Meta’s Frontier Ambitions

The models’ pricing places them in the premium AI segment. Through the Meta Model API, Muse Wonder will cost $4 per million input tokens and $20 per million output tokens, putting it in a similar pricing tier to GPT-6 Sol and Opus 5.5.

Muse Wonder Full, by contrast, will not be offered through the API. Meta says it will be exclusive to Muse Code subscribers, tying access to its most advanced model to a premium product. Both models will also power the Muse agent.

Long-Term Memory Takes Centre Stage

Meta is increasingly positioning its AI strategy around assistants that can support users over time rather than respond only to individual prompts. A central part of that approach is what the company calls “active memory”: the ability to retain, organise and synthesise information provided by users across extended periods.

Meta points to the MemoryBench benchmark, which evaluates systems designed for continual learning and memory construction. The company says Muse Wonder now outperforms Fable, Opus and Astra on this measure.

“We put a tremendous amount of effort into making sure your model can remember what’s important to you over days, weeks, months, and beyond,” research scientist Kavya Kopparapu said in a launch video.

New Models Follow Meta’s AI Reorganisation

The launch is also significant within Meta’s broader AI strategy. It is the company’s first frontier-model release since Scale AI founder Alexandr Wang reorganised its AI operations in August 2025. Meta has since introduced Muse Spark and the Muse agent, while making fewer high-profile model announcements than competitors such as OpenAI and Anthropic.

The new models were trained at Meta’s Prometheus data centre in central Ohio, which came online earlier in 2026, according to the company. The facility highlights the growing importance of infrastructure in the race to develop advanced AI systems, where performance depends not only on algorithms and data but also on computing capacity, data-centre investment and access to capital.

As competition intensifies, Muse Wonder and Wonder Full represent a broader bet on larger models, persistent memory and premium AI products. Their longer-term significance will depend on how their reported capabilities translate into real-world performance and whether users find enough value to pay for access.

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