Data Leakage Prevention – Protecting Sensitive Information

When DuPont lost $400 million in intellectual property, it wasn’t because a hacker from the other side of the world infiltrated their system. The information was simply stolen by a former employee. Alarmingly, data loss incidents are not always caused by deliberate actions.

A file containing personal information accidentally attached to an email and sent to multiple recipients; financial data stored in a USB pen drive, accidentally left in a restaurant; or bank account data of colleagues, inadvertently posted on a company website – these are also some of the everyday causes of data loss.

A report done by research company Infowatch regarding global data leaks in 2010 showed that there were actually more accidental data leaks in that year compared to intentional ones. Accidental leaks comprised 53%, while intentional leaks comprised 42% (the rest were unidentified).

But even if they ?only? happened accidentally, breach incidents like these can still be very costly. The tens of thousands of dollars that you could sometimes end up paying in civil penalties (as in the case when you lose other people?s personal information) can just be the beginning. More costly than this is the loss of customer and investor confidence. Once you lose those, you could consequently lose a considerable portion of your business.

Confidential information that may already be leaking out right under your nose

With all the data you collect, process, exchange, and store electronically every day, your IT system has surely now become a storehouse of sensitive information. Some of them, you may be even taking for granted.

But imagine what would happen if any of the following trade secrets fell into the wrong hands: marketing plans, confidential customer information, pricing data, product development strategies, business plans, supplier information, source codes, and employee salaries.

These are not the only kind of data that you should be worried about. You could also get into trouble if your sloppy IT security fails to protect employee or client personal information such as their names; social security numbers; drivers license numbers; or bank account numbers and credit/debit card numbers along with their corresponding PINs.

In some countries, you could face onerous data breach notification requirements and heavy fines when these kind of data are involved.

There are now more holes to plug

It’s not just the different varieties of sensitive electronic information that you have to worry about. Because these data can take on different forms, i.e. data-at-rest, data-in-motion, and data-at-the-endpoints, you also need to take aim at different areas in your IT system.

Sensitive information can be found ?at rest? in each of your employees? hard disks, in your servers, storage disks, and in off-site backup disks. They can also be found ?in motion? in email, instant messaging, social networking messaging, P2P file sharing, ftp, http, and so on.

That’s not all. Your highly mobile workforce may have already introduced yet another high-risk area into your system: data-at-the-endpoints. This includes USB flash-disks, laptops, portable hard disks, CDs, and even smartphones.

The main challenge of data leak prevention

Having been made aware of the various aspects of data leakage, have you already come to grips with the extent of the task at hand?

There are two major things you need to do here to prevent data leakage.

One, you need to identify what data you have that can be considered as sensitive/confidential information. Of course you have financial information and employee salaries in your files. But do you also store personally identifiable information? Do you have trade secrets that are stored in electronic form?

Two, you need to pinpoint their locations. Are they only on your hard disks and laptops? Or have they made their way to flash drives, CDs/DVDs, or portable HDDs? Are they being transmitted through email or any other file transfer media?

The reason why you need to know what your sensitive data are as well as where they are is because you would like all efforts of securing them to be as efficient and unobtrusive as possible.

Let’s say, as a way of protecting your data, you decide to implement encryption. Since encryption can consume a lot of storage space and significantly reduce performance, it may be impractical to encrypt your entire database or all your files. For the same reason, you wouldn’t want to encrypt every single email that you send.

Thus, the best way would be to encrypt only the data that really need encryption. But again, you need to know what data needs to be encrypted and where those data can be found. That alone is no simple task.

Not only will you need to deal with the data you already have, you will also have to worry about the data that will go through your systems during the course of your day-to-day transactions.

Identifying sensitive data as it enters or leaves your system, goes through your network, or gets stored in your file system or database, and then applying the necessary security actions should be done automatically and intelligently. Otherwise, you could end up spending on a lot of man-hours or, worse, wasting them on a lot of false positives and negatives.

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The Future is Smarter with a Smart Meter

Traditionally, electricity and water meter consumption was measured via analogue meters. Utility billing was based on actual consumption units obtained from the meter by meter readers. This entailed physical visits to the metering point. Lots of challenges came with meter reading; talk of customers feeling their privacy is intruded, meter readers encountering hostile customers, dogs, closed gates. The result was estimated bills that were most often than not very high.

Smart meters can be dubbed as the ?next generation? type of meters. Smart meters send wireless electronic meter readings to one?s energy supplier automatically. There are both gas smart meters and electricity smart meters. Smart meters come with in-home displays, which give someone real-time feedback on their energy usage and the associated cost.

Smart meters communicate meter readings directly to utility companies therefore no one has to come to your home to read your meter; and neither are you required to submit meter readings yourself. This not only reduces costs, but leads to more accurate electricity bills practically eliminating estimated bills. Smart meters signal the end of estimated bills, and the end of overpaying or underpaying for energy.

Whereas a smart meter in itself does not save you money, the add-ons (in-home displays) that come with the smart meters and which give someone real-time feedback on their energy usage helps them to reduce the unnecessary energy use and this ultimately leads to better oversight into how to lower utility bills hence better management of one?s energy use.

In summary, a smart meter is a technology that enables energy consumers to see their energy as they use it, a technology where energy is displayed as it is being used and wireless ratings sent. Adoption of smart meters would mean the end of estimated energy bills.

Smart meters are also promising a smart future where all energy consuming devices can be connected to the internet and centrally controlled using computers or smartphones. This means one is able to switch off lights and other energy consuming devices from a central point, hence make savings and this will enable them to have greater control of their energy use, hence more comfort, convenience and life will be cheaper for all. This is the smarter future we are all looking forward to.

Migrating from CRM to Big Data

Big data moved to centre stage from being just another fad, and is being punted as the latest cure-all for information woes. It may well be, although like all transitions there are pitfalls. Denizon decided to highlight the major ones in the hope of fostering better understanding of what is involved.

Accurate data and interpretation of it have become increasingly critical. Ideas Laboratory reports that 84% of managers regard understanding their clients and predicting market trends essential, with accelerating demand for data savvy people the inevitable result. However Inc 5000 thinks many of them may have little idea of where to start. We should apply the lessons learned from when we implemented CRM because the dynamics are similar.

Be More Results Oriented

Denizon believes the key is focusing on the results we expect from Big Data first. Only then is it appropriate to apply our minds to the technology. By working the other way round we may end up with less than optimum solutions. We should understand the differences between options before committing to a choice, because it is expensive to switch software platforms in midstream. data lakes, hadoop, nosql, and graph databases all have their places, provided the solution you buy is scalable.

Clean Up Data First

The golden rule is not to automate anything before you understand it. Know the origin of your data, and if this is not reliable clean it up before you automate it. Big Data projects fail when executives become so enthused by results that they forget to ask themselves, ?Does this make sense in terms of what I expected??

Beware First Impressions

Big Data is just that. Many bits of information aggregated into averages and summaries. It does not make recommendations. It only prompts questions and what-if?s. Overlooking the need for the analytics that must follow can have you blindly relying on algorithms while setting your business sense aside.

Hire the Best Brains

Big Data?s competitive advantage depends on what human minds make with the processed information it spits out. This means tracing and affording creative talent able to make the shift from reactive analytics to proactive interaction with the data, and the customer decisions behind it.

If this provides a d?j? vu moment then you are not alone. Every iteration of the software revolution has seen vendors selling while the fish were running, and buyers clamouring for the opportunity. Decide what you want out first, use clean data, beware first impressions and get your analytics right. Then you are on the way to migrating successfully from CRM to Big Data.

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What Sub-Metering did for Nissan in Tennessee

When Nissan built its motor manufacturing plant in Smyrna 30 years ago, the 5.9 million square-foot factory employing over 8,000 people was state of art. After the 2005 hurricane season sky-rocketed energy prices, the energy team looked beyond efficient lighting at the more important aspect of utility usage in the plant itself. Let’s examine how they went about sub-metering and what it gained for them.

The Nissan energy team faced three challenges as they began their study. They had a rudimentary high-level data collection system (NEMAC) that was so primitive they had to transfer the data to spread-sheets to analyse it. To compound this, the engineering staff were focused on the priority of getting cars faster through the line. Finally, they faced the daunting task of making modifications to reticulation systems without affecting manufacturing throughput. But where to start?

The energy team chose the route of collaboration with assembly and maintenance people as they began the initial phase of tracking down existing meters and detecting gaps. They installed most additional equipment during normal service outages. Exceptions were treated as minor jobs to be done when convenient. Their next step was to connect the additional meters to their ageing NEMAC, and learn how to use it properly for the first time.

Although this was a cranky solution, it had the advantage of not calling for additional funding which would have caused delays. However operations personnel were concerned that energy-saving shutdowns between shifts and over weekends could cause false starts. ?We’ve already squeezed the lemon dry,? they seemed to say. ?What makes you think there?s more to come??

The energy team had a lucky break when they stumbled into an opportunity to prove their point early into implementation. They spotted a four-hourly power consumption spike they knew was worth examining. They traced this to an air dryer that was set to cyclical operation because it lacked a dew-point sensor. The company recovered the $1,500 this cost to fix, in an amazing 6 weeks.

Suitably encouraged and now supported by the operating and maintenance departments, the Smyrna energy team expanded their project to empower operating staff to adjust production schedules to optimise energy use, and maintenance staff to detect machines that were running without output value. The ongoing savings are significant and levels of shop floor staff motivation are higher.

Let’s leave the final word to the energy team facilitator who says, ?The only disadvantage of sub-metering is that now we can’t imagine doing without it.?

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