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Doing More With Less: A Risk-Based, Cost-Effective Approach to Holistic Security

April 2009 by Ulf Mattsson, CTO, Protegrity Corporation

Data security plans often center around the "more is better" concept. These call for locking everything down with the strongest available protection and results in unnecessary expenses and frequent availability problems and system performance lags. Alternatively, IT will sometimes shape their data security efforts around the demands of compliance and best practices guidance, and then find themselves struggling with fractured security projects and the never-ending task of staying abreast of regulatory changes.

There is a better way — a risk-based classification process that enables organizations to determine their most significant security exposures, target their budgets towards addressing the most critical issues and achieve the right balance between cost and security. In this article, I discuss the risk-analysis processes that can help companies achieve cost-savings while measurably enhancing their overall data security profile by implementing a holistic plan that protects data from acquisition to deletion.

Step 1: Determine Data Risk Classification Levels

The first step in developing a risk-based data security management plan is to determine the risk profile of all relevant data collected and stored by the enterprise, and then classify data according to its designated risk level. Sounds complicated, but it’s really just a matter of using common sense. 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 its value to your organization and the anticipated cost of its exposure — would business processes be impacted? Would it be difficult to manage media coverage and public response to the breach? Then assign a numeric value for each class of data; high risk = 5, low risk = 1. Classifying data precisely according to risk levels enables you to develop a sensible plan to invest budget and efforts where they matter most.

Step 2: Map the Data Flow

Data flows through a company, into and out of numerous applications and systems. A complete understanding of this data flow enables an enterprise to implement a cohesive data security strategy that will provide comprehensive protections and easier management resulting in reduced costs.

Begin by locating all the places relevant data resides including applications, databases, files, data transfers across internal and external networks, etc. and determine where the highest-risk data resides and who has or can gain access to it (see ‘attack vectors’ section below). Organizations with robust data classification typically use an automated tool to assist in the discovery of the subject data. Available tools will examine file metadata and content, index the selected files, and reexamine on a periodic basis for changes made. The indexing process provides a complete listing and rapid access to data that meets the defined criteria used in the scanning and classification process. Most often, the indices created for files or data reflect the classification schema of data sensitivity, data type, and geographic region. High risk data residing in places where many people can/could access it is obviously data that needs the strongest possible protection.

When the classification schema is linked to the retention policy, as described above, retention action can be taken based on file indices. Additionally, the reports based on the indices can be used to track the effectiveness of the data retention program.

While we’re discussing data retention policies, it’s important to remember that data disposal also needs to be a secure process; usually you’ll opt to delete, truncate or hash the data the enterprise no longer needs to retain.

Truncation will discard part of the input field. These approaches can be used to reduce the cost of securing data fields in situations where you do not need the data to do business and you never need the original data back again. It is a major business decision to destroy, truncate or hash the data. Your business can never get that data back again and it may be more cost effective to transparently encrypt the data and not impact current or future business processes. In addition, the sensitive data may still be exposed in your data flow and logs prior to any deletion or truncation step.

Hash algorithms are one-way functions that turn a message into a fingerprint, at least twenty bytes long binary string to limit the risk for collisions. The Payment Card Industry Data Security Standard (PCI DSS) provided standards for strong encryption keys and key management but is vague in different points regarding hashing. Hashing can be used to secure data fields in situations where you do not need the data to do business and you never need the original data back again. Unfortunately a hash will be non-transparent to applications and database schemas since it will require long binary data type string. An attacker can easily build a (rainbow) table to expose the relation between hash values and real credit card numbers if the solution is not based on HMAC and a rigorous key management system. Salting of the hash can also be used if data is not needed for analytics.

Done properly, data classification begins with categorization of the sensitivity of data (i.e., “public,”“sensitive,” “confidential,” etc). Classification goes on to include the type of data being classified, for example, “sensitive, marketing program,” and where applicable, the countries to which the data classification applies. The classification allows the organization to automate the routines for flagging, removing, or archiving applicable data. Pay particular attention when automating the removal of data; consider instead alerting the user privileges of data requiring attention.

Additionally, an understanding of where all the sensitive data resides usually results in a project to reduce the number of places where the sensitive 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, and requires a reduced investment in data protection costs.

Step 3: Understand Attack Vectors (Know Your Enemy)

Use your data risk classification plan and the data flow map, along with a good understanding of criminals favored attack vectors, to identify the highest risk areas in the enterprise ecosystem. Currently web services, databases and data-in-transit are at high risk. The type of asset compromised most frequently is online data, not offline data on laptops, back-up tapes, and other media. Hacking and malware proved to be the attack method of choice among cybercriminals, targeting the application layer and data more than the operating system. But these vectors change so keep an eye on security news sites to stay abreast of how criminals are attempting to steal data.

There are two countervailing trends in malware, both likely to continue. One trend is toward the use of highly automated malware that uses basic building blocks and can be easily adapted to identify and exploit new vulnerabilities. This is the malware that exploits unpatched servers, poorly defined firewall rules, the OWASP top ten, etc. This malware is really aimed as the mass market – SMEs and consumers. The other trend is the use of high-end malware which employs the “personal touch” – customization to specific companies, often combined with social engineering to ensure it’s installed in the right systems. This is the type of malware that got TJX, Hannaford, and now Heartland according to a recent report published on KnowPCI ( The point is: the more we create concentrations of valuable data, the more worthwhile it is for malware manufacturers to put the effort into customizing a “campaign” to go after specific targets. So, if you are charged with securing an enterprise system that is a prime target (or partner with/outsource to a business that is a major target) you need to ensure that the level of due diligence that you apply to data security equals or exceeds that expended by malicious hackers, who are more than willing to work really, really hard to access that data.

Reports about recent data breaches paint an ugly picture. In mid-March Heartland Security Systems has yet, they claim, to be able to determine exactly how many records were compromised in the breach that gave attackers access to Heartland’s systems, used to process 100 million payment card transactions per month for 175,000 merchants. Given the size and sophistication of Heartland’s business—it is one of the top payment-processing companies in the United States—computer-security experts say that a standard, in-the-wild computer worm or Trojan is unlikely to be responsible for the data breach. Heartland spokespeople have said publicly that the company believes that the break-in could be part of a "widespread global cyber fraud operation."

According to a report in Digital Transactions and other news sources, in January 2009 Heartland apparently managed to find malware neatly tucked away on one of its payment-processing platforms after learning late in the Fall of 2008 that company might have a data breach in which unencrypted card numbers were captured during the authorization process. The key question here for many security professionals is why and how it took so long to find the malware. A post on a Wired News security blog, claiming to come from a Heartland employee stated that Heartland "might have caught it, or even prevented it, if we’d known what the government and the involved companies knew about some of the other recent breaches, but that data hadn’t been shared with us." Unfortunately that problem is being repeated again, with virtually no “lessons learned” information released about the Heartland breach.

What we have ‘learned” is something that many of us already know — compliance does not equal security. Credit-card payment processers such as Heartland are already bound to follow a set of security standards known as the Payment Card Industry Data Security Standard (PCI DSS), covering issues such as maintaining secure networks, protecting stored cardholder data, and keeping antivirus software up to date. Heartland was certified as PCI compliant last year, and other recent victims of break-ins, including RBS Worldpay, can make similar claims. The latest news reports that the malware was set to grab and transmit data — possibly looking for transmissions that represented authorization requests that were unencrypted while in transit over private networks. So Heartland could have been 100% compliant with PCI DSS, while its systems harbored a known weakness in the standard that hackers have now targeted.

Bill Homa, who stepped down as the CIO for the Hannaford retail chain after the company suffered a data breach in February 2008 that exposed 4.2 million payment card records , told Storefront Backtalk ( that he considers Microsoft’s OS to be “full of holes … If you limit your exposure to Microsoft, you’re going to be in a more secure environment, adding that Microsoft’s philosophy is decentralized, forcing IT to manage more points. That means more license fees for Microsoft and more potential security gotchas for the CIO.” He also said that he thinks it is astonishing that current PCI regulations do not require end-to-end encryption. Homa also added that he believes “there’s no such thing as a secure network … If you think your network is secure, you’re delusional."

That brings us back to our risk-based plan to protect data itself rather than focusing all our attention on securing the systems that the data resides on.

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 Business 2008 Data Breach Investigations Report ( were instigated by external sources. Just 18% of the breaches were caused by insiders; however, the insider incidences were much larger in terms of the amount of data compromised.

The cases included in this study encompass an astounding 230 million compromised records, a large portion of publicly disclosed records were breached during the four-year time frame of the study. The average number of records per breach was approximately 1.2 million. The median, however, is much lower at 45,000, indicating a skew in the dataset toward a few very large breaches. Even so, over 15 percent of cases involved more than 1 million records. Some type of cardholder data was compromised in 84 percent of cases. Obviously these statistics correlate to the financial motivation of the criminals. Related findings support this statement, as fraudulent use of stolen information was detected following 79 percent of breaches. Additionally, 32 percent of cases involved one of the many types of personally identifiable information (PII). This is likely attributable to the usefulness of this type of data for committing fraud and other criminal activities.

Step 4: Chose Cost-Effective Protections

Cost-cutting is typically accomplished in one of two ways: reducing quality or by getting the most out of a business’ investment. Assuming you’ve wisely opted for the latter, 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. Concerns about performance degradation, invasiveness, application support, and how to manage broad and heterogeneous database encryption implementations too often produce hard barriers to adopting this important security measure.

Some aspects to consider when evaluating data security solutions for effectiveness and cost-control include:

Access controls and monitoring

The threat from internal sources including administrators will require solutions that go beyond traditional access controls. Effective encryption solutions must provide separation of duties to prevent a DBA to get hold of the keys. A centralized solution can also provide the most cost effective strategy for an organization with a heterogeneous environment. Although some of the legal data privacy and security requirements can be met by native DBMS security features, many DBMSes do not offer a comprehensive set of advanced security options; notably, many DBMSes do not have separation of duties, enterprise key management, security assessment, intrusion detection and prevention, data-in-motion encryption, and intelligent auditing capabilities. This approach is suitable for protection of low risk data.


The basic idea behind tokens is that each credit card number that previously resided on an application or database is replaced with a token that references the credit card number. A token can be thought of as a claim check that an authorized user or system can use to obtain the associated credit card number. Rule 3.1 of the PCI standard advises that organizations “Keep cardholder data storage to a minimum.” To do so, organizations must first identify precisely where all payment data is stored. While this may seem simple, for many large enterprises it is a complicated task for a large enterprise the data discovery process can take months of staff time to complete. And then security administrators must determine where to keep payment data and where it shouldn’t be kept. It’s pretty obvious that the fewer repositories housing credit card information, the fewer points of exposure and the lower the cost of encryption and PCI initiatives. In the event of a breach of one of the business applications or databases only the tokens could be accessed, which would be of no value to a would-be attacker. All credit card numbers stored in disparate business applications and databases are removed from those systems and placed in a highly secure, centralized tokenization server that can be protected and monitored utilizing robust encryption technology.

Tokenization is a very hot “buzzword” but it still means many things to different people, and some implementations of it can pose an additional risk relative to mature encryption solutions. Companies are still being required to implement encryption and key management systems to lock down various data across the enterprise, including PII data, transaction logs and temporary storage. A tokenization solution would require a solid encryption and key management system to protect the tokenizer. Organizations use card numbers and PII data in many different places in their business processes and applications that would need to be rewritten to work with the token numbers instead. The cost for changing the application code can be hard to justify by the level of risk reduction. The risk of changing already working application code can also be hard to justify. This approach is suitable for protection of high risk data. Please see the discussion of tokenization in .

File level Database Encryption

File level Database Encryption has been proven to be fairly straight forward to deploy and with minimal impact on performance overhead, while providing convenient key management. This approach is cost effective since it installs quickly in a matter of days, utilizes existing server hardware platforms and can easily extend the protection to log files, configuration files and other database output. This approach is the fastest place to decrypt as it is installed just above the file system and encrypts and decrypts data as the database process reads or writes to its database files. This enables cryptographic operations in file system by block chunks instead of individually, row-by-row since the data is decrypted before it is read into the database cache. Subsequent hits of this data in the cache incur no additional overhead. Neither does the solution architecture diminish database index effectiveness, but remember that the index is in clear text and unprotected within the database.

This approach can also selectively encrypt individual files; and does not require that "the entire database" be encrypted. Database administrators can assign one or more tables to a table space file, and then policies can specify which table spaces to encrypt. Therefore, one need only encrypt the database tables that have sensitive data, and leave the other tables unencrypted. However, some organizations choose to encrypt all of their database files because there is little performance penalty and no additional implementation effort in doing so.

Production database requirements often use batch operations to import or export bulk data files. If these files contain sensitive data, they should be encrypted when at rest no matter how short the time they are at rest. (Note: some message queues such as MQ Series write payload data to a file if the message queue is backed up, sometime for a few seconds or up to hours if the downstream network is unavailable) It may be difficult to protect these files with column level encryption solutions. This approach can encrypt while still allowing transparent access to authorized applications and users.

This approach is suitable for protection of low risk data. Be aware of the limitations with this approach in the areas of no separation of DBA duties and potential issues that operating system patches can cause. File encryption doesn’t protect against database-level attacks. How are you going to effectively and easily keep administrators from seeing what they don’t need to see with file-level encryption? Protection of high risk data is discussed below in the sections ‘Field level encryption’ and ‘End-to-end encryption’.

Experience from some organizations has shown that the added performance overhead for this type of database encryption is often less than 5%. However, before deciding on any database file encryption solution, you should test its performance in the only environment that matters: your own.

Field level encryption and end-to-end encryption

Field level full or partial encryption/tokenization can provide cost effective protection of data fields in databases and files. Most applications are not operating on and should not be exposed to all bytes in fields like credit card numbers and social security numbers, and for those that do require full exposure an appropriate security policy with key management and full encryption is fully acceptable. This approach is suitable for protection of high risk data.

Continuous protection via end-to-end encryption at the field level is an approach that safeguards information by cryptographic protection or other field level protection from point-of-creation to point-of deletion to keep sensitive data or data fields locked down across applications, databases, and files - including ETL data loading tools, FTP processes and EDI data transfers. ETL (Extract, Transform, and Load) tools are typically used to load data into a data warehousing environments. This end-to-end encryption may utilize partial encryption of data fields and can be highly cost effective for selected applications like an e-business data flow.

End-to-end encryption is an elegant solution to a number of messy problems. It’s not perfect; field-level end-to-end encryption can, for example, break some applications, but its benefits in protecting sensitive data far outweigh these correctable issues. But the capability to protect at the point of entry helps ensure that the information will be both properly secured and appropriately accessible when needed at any point in its enterprise information life cycle.

End-to-end data encryption can protect sensitive fields in a multi-tiered data flow from storage all the way to the client requesting the data. The protected data fields may be flowing from legacy back-end databases and applications via a layer of Web services before reaching the client. If required, the sensitive data can be decrypted close to the client after validating the credentials and data-level authorization.

Today PCI requires that if you’re going outside the network, you need to be encrypted, but it doesn’t need to be encrypted internally. If you add end-to-end encryption, it might negate some requirements PCI have today, such as protecting data with monitoring and logging. Maybe you wouldn’t have to do that. So PCI Security Standards Council is looking at that in 2009. Data encryption and auditing/monitoring are both being necessary for a properly secured system - not one vs. the other. There are many protections that a mature database encryption solution can offer today that cannot be had with some of the monitoring solutions that are available. Installing malicious software on internal networks to sniff cardholder data and export it is becoming a more common vector for attack, and by our estimates is the most common vector of massive breaches, including TJX, Hannaford, Heartland and Cardsystems.

Storage-layer encryption or file layer encryption doesn’t provide the comprehensive protection that we need to protect against these attacks. There is a slew of research indicating that advanced attacks against internal data flow (transit, applications, databases and files) is increasing, and many successful attacks were conducted against data that the enterprise did not know was on a particular system. Using lower-level encryption at the SAN/NAS or storage system level can result in questionable PCI compliance, and separation of duties between data management and security management is impossible to achieve. Please see the discussion of end-to-end encryption in .

Compensating controls

PCI compensating controls are temporary measures you can use while you put an action plan in place. Compensating controls have a “shelf life” and the goal is to facilitate compliance, not obviate it. The effort of implementing, documenting and operating a set of compensating controls may not be cost effective in the long run. This approach is only suitable for temporary protection of low risk data.

Software based encryption

Many businesses also find themselves grappling with the decision between hardware-based and software-based encryption. Vendors selling database encryption appliances have been vociferously hawking their wares as a faster and more-powerful alternative to software database encryption. Many organizations have bought into this hype based on their experiences with hardware-based network encryption technology. The right question would be about the topology or data flow. The topology is crucial. It will dictate performance, scalability, availability, and other very important factors. The topic is important but the question is usually not well understood. Usually, hardware-based encryption is remote and software-based encryption is local but it doesn’t have anything to do with the form factor itself. Instead, it is about where the encryption is happening relative to your servers processing the database information.

Software to encrypt data at the table or column levels within relational database management systems is far more scalable and performs better on most of the platforms in an enterprise, when executing locally on the database server box. Software based encryption combined with an optional low cost HSM for key management operations will provide a cost effective solution that proves to be scalable in an enterprise environment.

The most cost effective solutions can be deployed as software, a soft appliance, a hardware appliance or any combination of the three, depending on security and operational requirements for each system. The ability to deploy a completely "green" solution, coupled with deployment flexibility, make these solution alternatives very cost effective also for shared hosting and virtual server environments. The green solution is not going away. There’s too much at stake.

In a data warehouse users may search among 100 million encrypted records or maybe five billion records. I’s crucial how much time is consumed for each decryption since a person may wait for hundreds or millions of records to be decrypted before the answer come back. If you do it locally, close to the data, you may have a response time of around five micro-seconds for each record and then you multiply by 100 million if you have 100 million records and so on. Compare those five micro-seconds for local encryption to the case of remote encryption. You may have a thousand times greater latency and total processing time, so if you add up that time the user may wait for an hour instead of one second.

In online transaction processing, one user may not see a difference between local and remote encryption. If one user is looking for one record in the database, the difference between five microseconds and five thousand microseconds is not noticeable. But if you have a high volume of processing on your website or data warehouse it will matter. If you add up all of your transactions and each of them takes a thousand times longer than necessary, you will hit multiple resource constraints and you will overload your computer. It can really cripple the user’s experience and business operations. It is interesting also to notice that a fast network doesn’t really help you. If you summarize all the steps that need to be processed for the data to go all the way from the database, over to another appliance and back, that path length is so much higher that the network speed doesn’t really help you.

Another thing to think about when you dissect a remote appliance solution is this; if you want to be secure, you actually need to encrypt the data travelling over the wire between the appliance and your database server. Guess what? It costs you more overhead to encrypt that traffic than to do the encryption in the first place. Another myth is that the speed and the power of the appliance is going to affect the total speed of the encryption and decryption processing. The marketers will say, “Well, we can stack appliances so that you can harness this enormous power of these boxes. Put it on a fast network and you can really offload the processing.” Seems to make sense at first, until all of the factors above are taken into consideration.

Build vs. buy

Many projects that have made the build vs. buy decision purely based on the misconceived notions from management about one option or the other. This is a decision that requires analysis and insight. Why re-invent the wheel if several vendors already sell what you want to build? Use Build or Buy Analysis to determine whether it is more appropriate to custom build or purchase a product. When comparing costs in the buy or build analysis, include indirect as well as direct costs in both sides of the analysis. For example, the buy side of the analysis should include both the actual out-of-pocket cost to purchase the packaged solution as well as the indirect costs of managing the procurement process. Be sure to look at the entire life-cycle costs for the solutions.

• Is the additional risk of developing a custom system acceptable?
• Is there enough money to analyze, design, and develop a custom system?
• Does the source code have to be owned or controlled?
• Does the system have to be installed as quickly as possible?
• Is there a qualified internal team available to analyze, design, and develop a custom system?
• Is there a qualified internal team available to provide support and maintenance for a custom developed system?
• Is there a qualified internal team available to provide training on a custom developed system?
• Is there a qualified internal team available to produce documentation for a custom developed system?
• Would it be acceptable to change current procedures and processes to fit with the packaged software?

Outsourcing Outsourcing can be a less cost effective approach in the long run and it will not solve the liability aspect. Outsourcing may also raise worries regarding security, hidden costs, loss of IT control, network bandwidth issues, lack of interoperability, vendor lock-in, service level agreements and data comingled across different business.

In many cases it may be more effective from a cost and data security standpoint to protect the data in the current system without changing the applications or the infrastructure. Recent incidents suggest that cybercrooks are increasingly beginning to target payment processors. Attacking a processor is much more serious than attacking a retailer. A processor sits at the nerve centre of the payment process and processes and also potentially store far more payment card data than any retailer. On the formal accounting side, outsourcing can be charged as expense, whereas the cost of developing an in-house system is capitalized, and may affect the capital budget.

Also look for efficiency gains when evaluating the cost-effectiveness of solutions. Centralized management of encryption keys reduces costs and complexity as well as potentially reducing system down time. Centralized policy enforcement, reporting and alerting supports compliance efforts and simplifies management chores as well as reducing risk and the costs of producing reporting for auditors.

A cost effective approach can be to use a single solution and process to provide a high quality of data across development, testing and staging environments and to protect sensitive data across development, testing, staging and production environments.

Step 5: Deployment

Focus initial efforts on hardening the areas that handle critical data and are a high-risk target for attacks. Continue to work your way down the list, securing less critical data and systems with appropriate levels of protection.

Be aware though that the conventional “Linked Chain” risk model used in IT security — the system is a chain of events, where the weakest link is found and made stronger — isn’t the complete answer to the problem. There will always be a weakest link. Layers of security including integrated key management, identity management and policy-based enforcement as well as encryption of data throughout its entire lifecycle are essential for a truly secure environment for sensitive data.

It is critical to have a good understanding of the data flow in order to select the optimal protection approach at different points in the enterprise. By properly understanding the dataflow we can avoid less cost effective point solutions and instead implement an enterprise protection strategy. A holistic layered approach to security is far more powerful than the fragmented practices present at too many companies. Think of your network as a municipal transit system – the system is not just about the station platforms; the tracks, trains, switches and passengers are equally critical components.

Many companies approach security as if they are trying to protect the station platforms, and by focusing on this single detail they lose sight of the importance of securing the flow of information. It is critical to take time from managing the crisis of the moment to look at the bigger picture. One size doesn’t fit all in security so assess the data flow and risk environment within your company and devise a comprehensive plan to manage information security that dovetails with business needs. Careful analysis of use cases and the associated threats and attack vectors can provide a good starting point in this area.

It is important to note that implementing a series of point solutions at each protection point will introduce complexity that will ultimately cause significant rework. Protecting each system or data set as part of an overall strategy and system allows the security team to monitor and effectively administer the encryption environment including managing keys and key domains without creating a multi-headed monster that is difficult to control.

Centralized management of encryption keys can provide the most cost effective solution for an organization with multiple locations, heterogeneous operating systems and databases. All standards now require rotation of the Data Encryption Keys (DEK’s) annually and some organizations choose to rotate some DEKs more frequently (such as a disconnected terminal outside the corporation firewall such as a Point of Sale system).

Manual key rotation in a point solution would require an individual to deliver and install new keys every year on all the servers. Automated key rotation through a central key management system reduces most of this cost and can potentially reduce the down time. Distributed point solutions for key management would include an initial investment for each platform, integration effort, maintenance and operation of several disparate solutions. It is our experience that manual key rotation in a point solution environment inevitably leads to increased down time, increase resource requirements, and rework. Key management and key rotation is an important enabler for several of the protection methods discussed above. Please see for more information on that topic.

Centralized management of reporting and alerting can also provide a cost effective solution for an organization with multiple heterogeneous operating systems and databases. This solution should track all activity, including attempts to change security policy, and encrypted logs to ensure evidence-quality auditing. Just as the keys should not be managed by the system and business owners, they should not have access to or control over the reporting and alerting logs and system A system with manual or nonexistent alerting and auditing functionality can increase the risk of undetected breaches and increase audit and reporting costs.

Although it’s always admirable to get the most for less, it’s important to keep the return on data security investments in perspective. A recent report by the Ponemon Institute, a privacy and information management research firm, found that data breach incidents cost $202 per compromised record in 2008, with an average total per-incident costs of $6.65 million. Find the right balance between cost and security by doing a risk analysis. For example field level encryption with good key management may lower the probability of card exposure (for example from 2% to 1% for a given year). A breach cost may be viewed to be $200 per card ($30 - $305 according to Gartner and Forrester, April, 2008). If 1 million cards would be exposed, an appropriate investment in a file protection solution with an integrated and sophisticated key management and protection system would be about $2 million.

All security spend figures produced by government and private research firms indicate that enterprises can put strong security into place for significantly less expenditure — about 10% the average cost of a breach. That’s a good figure to remember when the accounting hatchet seems poised to descend on data security budgeting.

Conclusion: Risk-based prioritization replaces 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 — with a thought-out logical plan that takes into account long range costs and benefits as well as enabling enterprises to target their budgets towards addressing the most critical issues first. It’s a balanced approach that delivers the enhanced security, reduced costs and labor with the least impact on business processes and the user community.

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