It’s been a while since we launched Above Trends, Beyond Insights. What started as an experiment in rethinking our editorial voice has now become a steady practice: every issue, we scan the signals that matter and translate them into business insight. Our commitment remains the same (less noise, more signal), with an even stronger focus on what executives need to navigate uncertainty: timely context, curated trends, and practical guidance.

AI transparency on TikTok and the shifting expectations for algorithmic governance

TikTok’s new controls for AI-generated content, together with the introduction of an invisible watermark, are more than a cosmetic update to the interface.

They show how one of the most influential platforms among younger users is starting to treat synthetic media as a specific category, with its own rules and signals, rather than as a seamless part of the feed.

Users can now limit how much AI-generated material they see, while the watermark infrastructure aims to make it easier to recognise when content has been produced by a model instead of a human creator.

These changes are unfolding in a European environment where regulators, journalists and civil society are increasingly concerned about information integrity, deepfakes and the opacity of recommendation engines. The practical outcome is a new kind of negotiation: platforms want to preserve engagement, regulators want clearer labelling and citizens want to understand whether what they see is trustworthy.

What is at stake goes beyond business and advertising strategies, because it affects how opinions are shaped, how news circulates and how people learn to interpret what appears on screen. The way TikTok and similar platforms design transparency around AI-generated content will influence media literacy, public debate and our collective capacity to distinguish between authentic expression, automated output and deliberate manipulation.

TREND TRACKER

Synthetic-media auditing in a world of AI-generated content

Synthetic-media auditing focuses on how digital content is created, transformed and shared, and on how this journey can be traced in a reliable way. It combines provenance metadata, watermarking and policy controls so that organisations can understand which images, videos or texts come from generative models and how they have been edited over time.

The emerging European Code of Practice on labelling AI-generated content, together with research on watermarking techniques and ENISA’s guidance on secure AI practices, shows that this is becoming a shared discipline rather than an experimental niche. The trend points toward a future where synthetic-media auditing is part of everyday editorial, marketing and security workflows, especially in sectors where trust and information integrity are essential.

Want to learn more?

• European Commission - Code of Practice initiative

• ENISA - Multilayer Framework for Good Cybersecurity Practices for AI

• Cornell University - Adoption of Watermarking for Generative AI Systems in Practice

QUICK INSIGHT

A twenty-minute framework to assess an AI vendor with confidence

AI procurement decisions are often made under time pressure, yet they define how data, risk and governance will be handled for years. A short, structured checklist helps leadership teams ask the right questions before committing.

• Review governance documentation and lifecycle management for the model.
  Ask for clear descriptions of how the model is designed, trained, evaluated and maintained over time, including versioning practices and change logs. Check whether there is a formal process for decommissioning or replacing models that no longer meet performance or risk criteria.

• Request clarity on dataset provenance, testing methods and quality safeguards.
  Ask where training and fine-tuning data come from, which licences apply and how sensitive information is handled. Verify how the vendor validates datasets, manages bias and tests the model across different scenarios that resemble your real-world use cases.

• Examine mitigation strategies for errors and hallucinations.
  Request concrete examples of how the system behaves when it does not know the answer, how guardrails are implemented and which escalation paths exist. Make sure you understand logging, feedback loops and the mechanisms that allow you to correct or override problematic outputs.

• Verify data-handling practices, retention policies and security boundaries.
  Clarify whether your data is used for further training, how it is isolated from other clients and where it is stored geographically. Review retention periods, encryption practices and access controls, and verify that these commitments are reflected in contracts and technical documentation.

• Assess contractual responsibilities, SLA transparency and independent audit options.
  Examine how uptime, response times and incident handling are defined, and what happens in case of data breaches or regulatory non-compliance. Ask whether third-party audits, certifications or penetration tests are available, and consider making periodic independent assessments part of the agreement.

WHO IS BABINI MAZZARI

Our Value Proposition

Babini Mazzari is the strategic IT partner for European companies looking to navigate digital transformation in a structured, pragmatic, and sustainable way.
We don’t just deliver technical solutions - we work as an extension of your internal team, helping you integrate systems, optimize processes, and lead change with clarity and competence.

Our approach is built on listening, transparency, and a strong results-driven culture. Whether you're scaling, modernizing, or rethinking your operating model, we support every client with the right tools, clear methodology, and long-term vision.
Above Technology. Beyond Solutions.

📎 [Our services]
📎 [Our Manifesto]