News & Insights

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16.07.2026

NIS2 Tools for SMEs: why tools help, but cannot buy compliance

No tool automatically makes a company NIS2-compliant. Tools only accelerate implementation if risks, responsibilities and processes have been clarified first. We show which tool categories you really need, what a pragmatic basic stack looks like and which questions you should answer before making any purchase.

03.07.2026

Data protection & IT security in M&A: review framework for data value, risks and deal implications

Data increases enterprise value only if it is economically usable, legally exploitable and technically protected. Missing legal bases, weak IT security or unclear data quality can turn what appears to be a value driver into a concrete deal risk. We explain how private equity investors and deal teams can systematically assess data value, data protection and IT security in due diligence and translate the findings into concrete deal mechanics.

25.06.2026

Deploying AI agents lawfully: fom hallucination risks to privacy by design

AI agents can perform tasks independently, make decisions and interact with IT systems — and that is precisely what makes them challenging from a compliance perspective. Deleted databases, legally binding chatbot statements and autonomous smear campaigns show that the risks are no longer theoretical. We explain which legal requirements apply and how companies can use AI agents in a legally compliant manner.

04.06.2026

Cookie banners in 2026: what must they be capable Of and Is reform on the horizon?

Cookie banners are intended to safeguard data protection, yet many users now click “Accept” out of sheer frustration. At the same time, pressure for reform is growing at EU level. What does this mean for companies? And what requirements actually apply today? An overview of the legal framework, practical challenges, and available courses of action.

29.05.2026

7 tips for more efficient data protection: why less Is sometimes more

Data protection does not have to be cumbersome in order to work in a legally robust way. Companies that clarify roles, simplify processes and prioritise risks in a targeted manner relieve the burden on data protection teams and make data protection more effective across the organisation.

20.05.2026

Vulnerability management: AI forces companies to rethink

The German Federal Office for Information Security (BSI) is warning that AI-supported systems could soon be able to identify security vulnerabilities at scale. For companies, this is no longer a future scenario. It is a reason to fundamentally rethink vulnerability management now. And the CRA clock is already ticking.

12.05.2026

The external CISO in the financial sector: between DORA, cyber risks and ICT risk management

Financial institutions are under particular pressure: cyber risks are increasing, DORA is tightening requirements, and the management body remains responsible. An external CISO can help establish information security in a strategic, pragmatic and audit-ready way.

07.05.2026

NIS2 risk management: not all risks are equally important

Cyber risks are on the rise, compliance pressures are mounting – and NIS2 makes risk management a mandatory requirement. Those who merely piece together individual measures are not thinking long-term. What is crucial is a manageable process that identifies risks, clarifies responsibilities and embeds security into day-to-day operations.

24.04.2026

Data capabilities: the foundation for AI, compliance and real business value

Many companies invest in data, tools and AI and then wonder why the breakthrough never comes. The reason often lies deeper: they lack structured data capabilities. What exactly does this mean, why are data capabilities decisive for scalability, compliance and monetisation and how do maturity levels make the difference?

17.04.2026

Data strategy: the underrated success factor for scalable AI projects

Many companies invest in AI tools, yet still fail due to unreliable models, lack of acceptance, or regulatory hurdles. The real key lies in having a robust data strategy. This article explains why a robust data strategy is crucial for the success of AI projects and how to build one effectively.