Do You Use DLP? We Should Talk

As an analyst, I’ve been covering DLP since before there was anything called DLP. I like to joke that I’ve talked with more people that have evaluated and deployed DLP than anyone else on the face of the planet. Yes, it’s exactly as exciting as it sounds. But all those references were fairly self-selected. They’ve either been Gartner clients, or our current enterprise clients, that were/are typically looking for help in product selection or dealing with some sort of problem. Many of the rest are vendor-supplied references. This combination skews the conversations towards people picking products, people with problems, or those a vendor think will make them look good. I’m currently working on an article for Information Security magazine on “Real-World DLP”, and I’m hunting for some new references to expand that field a bit. If you are using DLP, successfully or not, and are willing to talk confidentially, please drop me a line. I’m looking for real-world stories, good and bad. If you are willing to go on the record, we’re also looking for good quote sources. The focus of the article is more on implementation than selection, and will be vendor-neutral. To be honest, one reason I’m putting this out in the open is to see if my normal reference channels are skewed. It’s time to see how our current positions and assumptions play out on the mean streets of reality. Of course I’ll be totally pissed if I’ve been wrong this entire time and have to retract everything I’ve ever written on DLP. **Update – Oh yeah, my email address is rmogull, that is with two ‘L’s, at securosis dot com. Please let me know. Share:

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The Business Justification for Data Security: Understanding Potential Loss

Rich posted the full research paper last week, but as not everyone wants to read the full 30 pages, we decided to continue posting excepts here. We still encourage comments as this will be a living document for us, and we will expand in the future. Here is Part Four: Understanding Potential Losses Earlier we deliberately decoupled potential losses from risk impact, even though loss is clearly the result of a risk incident. Since this is a business justification model rather than a risk management model, it allows us to account for major types of potential loss that are the result of multiple types of risk and simplifies our overall analysis. We will highlight the major loss categories associated with data security and, as with our risk estimates, break them out into quantitative and qualitative categories. These loss categories can be directly correlated back to risk estimates, and it may make sense to walk through that exercise at times, but as we complete our business justification you’ll see why it isn’t normally necessary. If data is stolen in a security breach, will it cost you a million dollars? A single dollar? Will you even notice? Under “Data Loss Models”, we introduced a method for estimating values of the data your company possess to underscore what is at stake. Now we will provide a technique for estimating costs to the business in the event of a loss. We look at some types of loss and their impacts. Some of these have hard costs that can be estimated with a reasonable degree of accuracy. Others are more nebulous, so assigning monetary values doesn’t make sense. But don’t forget that although we may not be able to fully quantify such losses, we cannot afford to ignore them them, because unquantifiable costs can be just as damaging. Quantified vs. Qualified Losses As we discussed with noisy threats, it is much easier to justify security spending based on quantifiable threats with a clear impact on productivity and efficiency. With data security, quantification is often the rare exception, and real losses typically combine quantified and qualified elements. For example, a data breach at your company may not be evident until long after the fact. You don’t lose access to the data, and you might not suffer direct losses. But if the incident becomes public, you could then face regulatory and notification costs. Stolen customer lists and pricing sheets, stolen financial plans, and stolen source code can all reduce competitive advantage and impact sales — or not, depending on who stole what. Data stolen from your company may be used to commit fraud, but the fraud itself might be committed elsewhere. Customer information used in identity theft causes your customers major hassles, and if they discover your firm was the source of the information, you may face fines and legal battles over liability. As these can account for a majority of total costs, despite the difficulty in obtaining an estimate of the impact, we must still account for the potential loss to justify spending to prevent or reduce it. We offer two approaches to combining quantified and qualified potential losses. In the first, you walk through each potential loss category and either assign an estimated monetary value, or rate it on our 1-5 scale. This method is faster, but doesn’t help correlate the potential loss with your tolerance. In the second method, you create a chart like the one below, where all potential losses are rated on a 1-5 scale, with either value ranges (for quantitative loss) in the cells, or qualitative statements describing the level of loss. This method takes longer, since you need to identify five measurement points for each loss category, but allows you to more easily compare potential losses against your tolerance, and identify security investments to bring the potential losses (or their likelihood) down to an acceptable level. Loss = 1 2 3 4 5 Notification costs (total, not per record) $0-$1000 $1,001-$10,000 $10,001-$100,000 $100,001-$500,000 >$500,00 Reputation Damage No negative publicity Single negative press mention, local/online only Ongoing negative press <2 weeks, local/online only, Single major outlet mention. Ongoing sustained negative press >2 weeks, including multiple major outlets. Measurable drop in customer activity. Sustained negative press in major outlets or on a national scale. Material drop in customer activity. Potential Loss Categories Here are our recommended assessment categories for potential loss, categorized by quantifiable vs. only qualifiable: Quantifiable potential data security losses: Notification Costs: CA 1386 and associated state mandates to inform customers in the event of a data breach. Notification costs can be estimated in advance, and include contact with customers, as well as any credit monitoring services to identify fraudulent events. The cost is roughly linear with the total number of records compromised. Compliance Costs: Most companies are subject to federal regulations or industry codes they must adhere to. Loss of data and data integrity issues are generally violations. HIPAA, GLBA, SOX, and others include data verification requirements and fines for failure to comply. Investigation & Remediation Costs: An investigation into how the data was compromised, and the associated costs to remediate the relevant security weaknesses, have a measurable cost to the organization. Contracts/SLAs: Service level agreements about quality or timeliness of services are common, as are confidentiality agreements. Businesses that provide data services rely upon the completeness, accuracy, and availability of data; falling short in any one area may violate SLAs and/or subject the company to fines or loss of revenues. Credit: Loss of data and compromise of IT systems are both viewed as indications of investment risk by the financial community. The resulting impact on interest rates and availability of funds may affect profitability. Future Business & Accreditation: Data loss, compliance failures, or compliance penalties may impair ability to bid on contracts or even participate in certain ventures due to loss of accreditation. This can be a permanent or temporary loss, but the effects are tangible. Note that future business is also a qualitative loss — here we

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