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Ulf Mattsson, CTO, Protegrity: Developing, Deploying and Managing a Risk-Adjusted Data Security Plan

December 2009 by Marc Jacob

Not too long ago, many security experts believed that the best way to defend data was to apply the strongest possible technological protections to all of the data, all of the time. While that plan may work perfectly in theory, in the real world of business this model creates unacceptable costs, performance and availability problems.

What works from both IT and management standpoints? Risk-adjusted data security. Protecting data according to risk enables organizations to determine their most significant security exposures, target their budgets towards addressing the most critical issues, strengthen their security and compliance profile, and achieve the right balance between business needs and security demands.

Other issues that risk-adjusted security addresses are the unnecessary expenses, availability problems and system performance lags that result when data is over-protected. And cloud-based technologies, mobile devices and the distributed enterprise require a risk-mitigation approach to security, focused on securing mission critical data, rather than the now-unachievable ‘protect all the data at all costs’ model of years past.

Here’s how to develop and deploy a risk-adjusted data protection plan:

1: Know Your Data

Begin by determining the risk profile of all relevant data collected and stored by the enterprise, and then classify that data according to its designated risk level. Data that is resalable for a profit — typically financial, personally identifiable and confidential information — is high risk data and requires the most rigorous protection; other data protection levels should be determined according to the value of the information to your organization and the anticipated cost of its exposure — would your business be impacted? Would it be difficult to manage media coverage and public response to the breach?

There are several models that a business can use to classify data. Larger enterprises will likely want to rely on policy-driven automated tools. Smaller businesses can use the simplest model: assign a numeric value for each class of data; high risk = 5, low risk = 1.

2: Find Your Data

Data flows through a company, into and out of numerous applications and systems. A complete understanding of the high risk data flow is essential to the risk-adjusted process. You can’t protect data if you don’t know where it is, and assigned risk levels will change depending on how data is being collected, used and stored. High risk data residing in places where many people have access is obviously data that needs the strongest possible protection.

Locate all of the places that data resides including applications, databases, files, and all the systems that connect these destinations such as data transfers across internal and external networks, etc. and determine where the highest-risks reside and who has or can gain access to data (see “Understand your Enemy” below).

Other areas to examine for data stores include your outsourcing partnerships as well as data that is being used for nonproduction purposes such as third-party marketing analysis or in test and engineering environments. It’s not uncommon for organizations to invest in protecting production systems and data centers yet have live data sitting unprotected on the systems of application developers and other outsourced parties. If live production data is being used in a less controlled environment there has to be attention paid to regulatory compliance and security threats. Here, too, data de-identification technologies like Format-Controlling Encryption and tokenization can help.

Step 3: Understand Your Enemy

The next step is conducting an end-to-end risk analysis on the high risk data flow to identify the highest risk areas in the enterprise ecosystem and the points where data might be exposed to unauthorized users. Currently web services, databases and data-in-transit are at high risk. The type of asset compromised most frequently is online data. Exploiting programming code vulnerabilities, subverting authorized user credentials and malware targeting the application layer and data (rather than the operating system) are the attack methods that are being utilized most frequently. These vectors change so keep an eye on security news sites to stay abreast of current threats.
Most data breaches are caused by external sources but breaches attributed to insiders, though fewer in number, typically have more impact than those caused by outsiders. Nearly three-quarters of the breaches examined in the Verizon Report were instigated by external sources. Unauthorized access via default credentials (usually third-party remote access) and SQL injection (against web applications) were the top types of hacking, access to a network was often followed by malware being planted on the system.

Step 4: Choose Your Defenses

Look for multi-tasking solutions that protect data according to its risk classification levels, supports business processes, and is able to be change with the environment so that you can easily add new defenses for future threats and integrate it with other systems as necessary.

High risk data is best secured using end-to-end encryption or tokenization of individual data fields. Tokenization removes sensitive data from the information flow at the earliest possible point in the process, replacing it with a token that acts as an alias for the protected data. By associating original data with an alias, high-risk data can systematically be removed and protected from malicious hackers over its lifecycle under a fully auditable and controllable process. This practical protection method is perfectly suited for securing high risk data like payment card information and social security numbers.

Newer solutions provide targeted protection for data in use and doesn’t interfere with business processes. For example, Data Format Controlling Encryption retains the original format, on a character-by-character basis, of encrypted data, putting an end to the data re-formatting and database schema changes required by other encryption techniques. It’s especially well-suited to protect data that’s being used for testing or development in a less-controlled environment. Partial encryption can then be applied to provide the ability to encrypt selected parts of a sensitive data field based on policy rules. Policy-Based Masking provides the ability to mask selected parts of a sensitive data field. Implemented at the database level rather than application level, policy-based Data Masking provides a consistent level of security across the enterprise without interfering with business operations and greatly simplifies data security management chores.

Step 5: Deployment

Risk-Adjusted data protection enables enterprises to stage their security roll-out. Focus your initial efforts on hardening the areas that handle critical data and are a high-risk target for attacks. Then continue to work your way down the risk-prioritized list, securing less critical data and systems with appropriate levels of protection.

Security is an ongoing process not a series of events. The level of protection required by data may change according to how it is being collected, transmitted, used and stored. Reevaluate risk levels annually and on an as-needed basis if business processes change.

Step 6: Crunch the Numbers

Risk-adjusted data security plans are cost effective. Among the typical benefits of a risk-adjusted plan is the elimination of the all too common and costly triage security model which is ineffective whether you’re triaging based on compliance needs or the security threat of the moment. Replacing triage with a well thought-out logical plan that takes into account long-range costs and benefits enables enterprises to target their budgets toward addressing the most critical issues.

By switching focus to a holistic view rather than the all too common security silo methodology, an enterprise will also naturally move away from deploying a series of point solutions at each protection point, which results in redundant costs, invariably leaves holes in the process, and introduces complexity that will ultimately cause significant and costly rework.

Additionally, an understanding of where data resides usually results in a project to reduce the number of places where sensitive data is stored. Once the number of protection points has been reduced, a project to encrypt the remaining sensitive data with a comprehensive data protection solution provides the best protection while also giving the business the flexibility it needs.

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