To help security pros plan their next decade of investments in data security, last year myself, John Kindervag, and Heidi Shey, researched and assessed 20 of the key technologies in this market using Forrester's TechRadar methodology. The resulting report, TechRadar™: Data Security, Q2 2014, became one of the team’s most read research for the year. However, it’s been a year since we finalized and published our research and it’s time for a fresh look.
One can argue that the entirety of the information security market - its solutions, services, and the profession itself - focuses on the security of data. While this is true, there are solutions that focus on securing the data itself or securing access to the data itself - regardless of where data is stored or transmitted or the user population that wants to use it. As S&R pros continue to pursue a shift from a perimeter and device-specific security approach to a more data- and identity-centric security approach, it’s worthwhile to hyper focus on the technology solutions that allow you to do just that....
Last year, we included the following 20 technologies in our research:
Big data and Hadoop (Yellow Elephants) are so synonymous that you can easily overlook the vast landscape of architecture that goes into delivering on big data value. Data scientists (Pink Unicorns) are also raised to god status as the only real role that can harness the power of big data -- making insights obtainable from big data as far away as a manned journey to Mars. However, this week, as I participated at the DGIQ conference in San Diego and colleagues and friends attended the Hadoop Summit in Belgium, it has become apparent that organizations are waking up to the fact that there is more to big data than a "cool" playground for the privileged few.
The perspective that the insight supply chain is the driver and catalyst of actions from big data is starting to take hold. Capital One, for example, illustrated that if insights from analytics and data from Hadoop were going to influence operational decisions and actions, you need the same degree of governance as you established in traditional systems. A conversation with Amit Satoor of SAP Global Marketing talked about a performance apparel company linking big data to operational and transactional systems at the edge of customer engagement and that it had to be easy for application developers to implement.
Hadoop distribution, NoSQL, and analytic vendors need to step up the value proposition to be more than where the data sits and how sophisticated you can get with the analytics. In the end, if you can't govern quality, security, and privacy for the scale of edge end user and customer engagement scenarios, those efforts to migrate data to Hadoop and the investment in analytic tools cost more than dollars; they cost you your business.
The CES Tech West Expo has a number of specific areas of coverage including fitness and health, wearables, connected home, family safety, and some young innovative companies located in the startup area of the section. I spent a few hours interviewing and discussing the Internet of Things (IoT) with as many vendors as I could find. I had many good laughs and shed a few tears during the process. To describe the process, the general communication would go something like this:
Me: "Can you point me at the most technical person you have at your booth? I'd like to talk about how you secure your devices and the sensitive / personal data that it accesses and collects."
Smartest tech person at the booth: "Oh! We are secure; we [insert security-specific line here]."
Me: "Never mind . . ." (dejected look on my face).
CMOs historically focused narrowly on marketing and promotion. That’s not enough in the age of the customer. The CMO of 2015 must own the most important driver of business success -- the customer experience -- and represent the customer’s perspective in corporate strategy. Andy Childs at Paychex is a great example -- he owns not only traditional marketing but strategic planning and M&A.
We are in a golden age of data breaches - just this week, the United States Post Office was the latest casualty - and consumer attitudes about data security and privacy are evolving accordingly. If your data security and privacy programs exist just to ensure you meet compliance, you’re going to be in trouble. Data (and the resulting insights) is power. Data can also be the downfall for an organization when improperly handled or lost.
In 2015, Forrester predicts that privacy will be a competitive differentiator. There is a maze of conflicting global privacy laws to address and business partner requirements to meet in today’s data economy. There’s also a fine line between cool and creepy, and often it’s blurred. Companies, such as Apple, are sensitive to this and adjusting their strategies and messaging accordingly. Meanwhile, customers — both consumers and businesses — vote with their wallets.
An IT mindset has dominated the way organizations view and manage their data. Even as issues of quality and consistency raise their ugly head, the solution has often been to turn to the tool and approach data governance in a project oriented manner. Sustainability has been a challenge, relegated often to IT managing and updating data management tools (MDM, data quality, metadata management, information lifecycle management, and security). Forrester research has shown that less than 15% of organizations have business lead data governance that is linked to business initiatives, objectives and outcomes. But, this is changing. More and more organizations are looking toward data governance as a strategic enterprise competence as they adopt a data driven culture.
This shift from project to strategic program requires more than basic workflow, collaboration, and data profiling capabilities to institutionalize data governance policies and rules. The conversation can't start with data management technology (MDM, data quality, information lifecycle management, security, and metadata management) that will apply the policies and rules. It has to begin with what is the organization trying to achieve with their data; this is a strategy discussion and process. The implication - governing data requires a rethink of your operating model. New roles, responsibilities, and processes emerge.
On May 5, 2014, Target announced the resignation of its CEO, Gregg Steinhafel, in large part because of the massive and embarrassing customer data breach that occurred just before the 2013 U.S. holiday season kicked into high gear. After a security breach or incident, the CISO (or whoever is in charge of security) or the CIO, or both, are usually axed. Someone’s head has to roll. But the resignation of the CEO is unusual, and I believe this marks an important turning point in the visibility, prioritization, importance, and funding of information security. It’s an indication of just how much:
Security directly affects the top and bottom line. Early estimates of the cost of Target's 2013 holiday security breach indicate a potential customer churn of 1% to 5%, representing anywhere from $30 million to $150 million in lost net income. Target's stock fell 11% after it disclosed the breach in mid-December, but investors pushed shares up nearly 7% on the news of recovering sales. In February 2014, the company reported a 46% decline in profits due to the security breach.
Poor security will tank your reputation. The last thing Target needed was to be a permanent fixture of the 24-hour news cycle during the holiday season. Sure, like other breached companies, Target’s reputation will likely bounce back but it will take a lot of communication, investment, and other efforts to regain customer trust. The company announced last week that it will spend $100 million to adopt chip-and-PIN technology.
According to recent Business Technographics data, half of US enterprise technology management professionals report that there is 1.) no way to gain a single view of status and availability across their portfolio of cloud services, 2.) that they don’t have a clear way to assess the risk of using a third-party public as-a-service offering, and/or 3.) that they have no way to manage how providers handle their data.
An interesting debate is ensuing regarding how to best protect cloud data, given the market landscape. So far two modalities are emerging:
·A. Inserting in-line encryption between the enterprise and the SaaS provider that encrypts and/or tokenizes all data before it goes to the cloud to ensure safety interoperating within public cloud systems.
·B. The human-firewall model, in which IT closely monitors activity with context/content analytics and anomaly detection tools.
The truth lies somewhere between the two. By carefully applying Forrester’s data security and control framework, clients should incrementally encrypt data deemed sensitive to compliance or regulation, such as credit card and Social Security numbers, and closely monitor all activity across users and cloud applications.
Many of us in the information security space have a proud legacy of only purchasing best in breed point solutions. In my early days as an information security practitioner, I only wanted to deploy these types of standalone solutions. One of the problems with this approach is that it results in a bloated security portfolio with little integration between security controls. This bloat adds unneeded friction to the infosec team’s operational responsibilities. We talk about adding friction to make the attacker’s job more difficult, what about this self-imposed friction? S&R pros jobs are hard enough. I’m not suggesting that you eliminate best in breed solutions from consideration, I’m suggesting that any “point solution” that functions in isolation and adds unneeded operational friction shouldn’t be considered.
For decades, firms have deployed applications and BI on independent databases and warehouses, supporting custom data models, scalability, and performance while speeding delivery. It’s become a nightmare to try to integrate the proliferation of data across these sources in order to deliver the unified view of business data required to support new business applications, analytics, and real-time insights. The explosion of new sources, driven by the triple-threat trends of mobile, social, and the cloud, amplified by partner data, market feeds, and machine-generated data, further aggravates the problem. Poorly integrated business data often leads to poor business decisions, reduces customer satisfaction and competitive advantage, and slows product innovation — ultimately limiting revenue.
Forrester’s latest research reveals how leading firms are coping with this explosion using data virtualization, leading us to release a major new version of our reference architecture, Information Fabric 3.0. Since Forrester invented the category of data virtualization eight years ago with the first version of information fabric, these solutions have continued to evolve. In this update, we reflect new business requirements and new technology options including big data, cloud, mobile, distributed in-memory caching, and dynamic services. Use information fabric 3.0 to inform and guide your data virtualization and integration strategy, especially where you require real-time data sharing, complex business transactions, more self-service access to data, integration of all types of data, and increased support for analytics and predictive analytics.
Information fabric 3.0 reflects significant innovation in data virtualization solutions, including: