A corporate data protection officer requires more than just a formal appointment. What is crucial is a robust operational model with clear responsibilities, resources, reporting structures, audits, and escalation procedures.
Privacy-IRM software should not be evaluated solely based on feature lists. What matters most is whether roles, workflows, evidence, permissions, and operational risks can actually be managed within the ongoing governance process.
AI requires clear governance processes: use cases, risks, roles, approvals, and documentation must be brought together in a structured manner so that AI can be used safely and transparently within the company.
Data protection records are only audit-ready if the processing, risk, approval, version, and measures remain traceably linked. Why email, Teams, and drives are often insufficient for this purpose.
Many AI projects get off to a fast start technically but lack organizational oversight. Why AI governance requires a collaborative decision-making process involving the CIO, DPO, Legal, Security, and business units.
There are processing records that look impressive. Neat columns. Well-defined terms. A last-modified date that isn't too embarrassing. During an audit, you can open them up, take a quick look at
When you enter into a business relationship, you want to know: „Can I really trust this provider?” The CyberVadis score from 2B Advice—920—answers exactly that question.
Many companies maintain their record of processing activities (RPA) in Excel. This works fine on a day-to-day basis—until the RPA is audited. We explain why Article 30 of the GDPR
Many companies are currently making a costly mistake: they are using AI in situations where it is structurally the wrong solution. This is because most business processes require