Defining your data via data discovery and classification is the foundation for data security strategy. The idea that you must understand what data you have, where it is, and if it is sensitive data or not is one that makes sense at a conceptual level. The challenge, as usual, is with execution. Too often, data classification is reduced to an academic exercise rather than a practical implementation. The basics aren’t necessarily simple, and the existing tools and capabilities for data classification continue to evolve.* Still, there are several best practices that can help to put you on the road to success:
Keep labels simple. At a high level, stick to no more than 3 or 4 levels of classification. This reduces ambiguity about what each classification label means. Lots of classification labels increases confusion and the chance for opportunistic data classification (where users may default to classifying data at a lower level for ease of access and use).
Recognize that there are two types of data classification projects: new data and legacy data. This will help to focus the scope of your efforts. Commit to tackling new data first for maximum visibility and impact for your classification initiative.
Identify roles and responsibilities for data classification. Consider data creators, owners, users, auditors (like privacy officers, or a risk and compliance manager), champions (who’s leading the classification initiative?). Data is a living thing and all employees have a role in classification. Classification levels may change over time as data progresses through its lifecycle or as regulatory requirements evolve.
The big public cloud providers, most of which are still from the United States, sometimes have a hard time finding ways to balance their legal obligations at home with the quite different sensitivities they encounter amongst their new international customers. For a long time, the toolkit has been pretty consistent: site data centres as close to the customer as possible, vehemently support political efforts to harmonize laws, and ocassionally be seen to stand up to the worst execesses of Government over-reach.
(Source: Flickr user Luigi Rosa. Image licensed under Creative Commons Attribution License)
Microsoft's announcements in Germany today appear, on the surface, to follow that model pretty closely. But there's a twist that's potentially very important as we move forward.
First, the standard bit. Microsoft, yesterday, announced new data centres will be operational in the UK next year, joining existing European facilities in Dublin and Amsterdam. Big competitor Amazon did much the same last week, announcing that a new UK data centre will be online in the UK by "2016 or 2017." Given the vague timescales, it might be easy to assume that Amazon was trying to steal a little of Microsoft's thunder with a half-baked pre-announcement. And then, today, Microsoft announced two new data centres in Germany. Amazon already has a facility there, of course.
As data flows between countries with disparate data protection laws, firms need to ensure the safety of their customer and employee data through regulatory compliance and due diligence. However, multinational organizations often find global data privacy laws exceedingly challenging. To help our clients address these challenges, Forrester developed a research and planning tool called the Data Privacy Heat Map (try the demo version here). Originally published in 2010, the tool leverages in-depth analyses of the privacy-related laws and cultures of 54 countries around the world, helping our clients better strategize their own global privacy and data protection approaches.
Regulation in the data privacy arena is far from static. In the year since we last updated the heat map, we have seen many changes to how countries around the world view and enforce data privacy. Forrester has tracked and rated each of these 54 countries across seven different metrics directly within the tool. Among them, seven countries had their ratings change over the past year. Some of the most significant changes corporations are concerned with involve:
New national omnibus data privacy laws spanning private and/or public industry. Data privacy regulation, when looked at globally, forms a spectrum of maturity beginning with spotty industry or situation-specific laws all the way to omnibus frameworks. As you might expect, responsible corporations prefer to engage in business practices where the data privacy laws are clearly-defined and transparent. For instance, countries such as Brazil and China are in the process of moving towards potential omnibus laws which will replace a multitude of sectoral and situation-based laws. Other countries, such as Colombia and Singapore, have recently passed far-reaching omnibus laws, also replacing a patchwork of prior sectoral laws.
Reflections from the 10th Safer Internet Day Conference in Berlin, February 5th 2013
Earlier this month, I had the pleasure of speaking at the Safer Internet Day Conference in Berlin, organized by the Federal Ministry of Consumer Protection, Food and Agriculture and BITKOM, the German Association for Information Technology, Telecommunication and New Media. The conference title, ‘Big Data – Gold Mine or Dynamite?’ set the scene; after my little introductory speech on what big data really means and why this is a relevant topic for all of us (industry, consumers, and government), the follow-up presentations pretty much focused either on the ‘gold mine’ or the ‘dynamite’ aspect. To come straight to the point: I was very surprised, if not slightly shocked at how deep a gap became visible between the industry on the one side and the government (mainly the data protection authorities) on the other side.
While industry representatives, spearheaded by the BITKOM president Prof. Dieter Kempf and speakers from IBM, IMS Health, SAS, and others, highlighted interesting showcases and future opportunities for big data, Peter Schaar, the Federal Commissioner for Data Protection, seemed to be on a crusade to protect ‘innocent citizens’ from the ‘baddies’ in the industry.