Data Warehouse control and security

DATA WAREHOUSE CONTROL AND SECURITY

Data Warehouse (DW) is a collection of integrated databases designed to support managerial decision-making and problem-solving functions. It contains both highly detailed and  summarized historical data relating to various categories, subjects or areas. All units of data are relevant to appropriate time horizons. DW is an integral part of enterprise—wide decision support system, does not ordinarily involve data updating. It empowers and end users perform data access and analysis. This eliminates the need for the IS function to perform informational processing from the legacy system for the end-users. It also gives an organization certain competitive advantages, such as: fostering a culture of information sharing; enabling employees to effectively and efficiently solve dynamic organizational problems; minimizing operating costs and maximizing the employee's turnovers.

The security requirements of the DW environments are not unlike those of other distributed computing systems. Thus, having an internal control mechanism to assure the confidentiality, integrity and availability of data in a distributed environment is of paramount importance. Unfortunately, most data warehouses are built with little or no consideration given to security during the development phase. Achieving proactive security requirements of DW there are seven phase processes:

• Identifying the data
• Classifying the data
• Quantifying the value of data
• Identifying data security vulnerabilities
• Identifying data protection measures and their costs
• Selecting cost-effective security measures
• Evaluating the effectiveness of security measures.

These phases are parts of an enterprise—wide vulnerability assessment and management program.

Identifying the Data

The first security task is to identify all digitally-stored corporate data placed in the Data Warehouse. This is an often ignored, but critical phase of meeting the security requirements of the DW environment since it forms the foundation for subsequent phases. It entails taking a complete inventory of all the data that is available to the DW end-users. The installed data monitoring software—an important component of the DW can provide an accurate information about all databases, tables, columns, rows of data and profiles of data residing in the DW environment as well as who is using the data and how often they use the data.

A manual procedure would require preparing a checklist of the same information described above. Whether the required information is gathered through an automated or a manual method, the collected information needs to be organized, documented and retained for the next phase.

Classifying the Data

Classifying all the data in the DW environment is needed to satisfy security requirements for the data confidentiality, integrity and availability in a prudent manner. In some cases, data classification is a legally mandated requirement. Performing this task requires the environment of the data owners, custodians and the end-users. Data is generally classified on the basis of criticality or sensitivity to disclosure, modification and destruction. The sensitivity of corporate data can be classified as least sensitive data, moderately-sensitive data, most sensitive data. Classifying data into different categories is not as easy as it seems.

Quantifying the Data 

In most organizations, senior management demands to see the smoking gun (e.g. Cost vs benefit figures, or hard evidence of committed frauds) before committing corporate funds to support security initiatives. Cynic managers will be quick to point out that they deal with hard reality —not soft variables connected hypothetically. Quantifying the value of sensitive data warranting protective measures is as close to the smoking gun as one can get the trigger senior manager's support and commitment to security initiative in the DW environment.

The quantifying process is primarily concerned about assigning "Street Value" to data grouped under different sensitivity categories. By itself, data has no intrinsic value. However, the definite value of data is often measurable by the cost to

(a) reconstruct lost data

(b) restore the integrity of corrupted, fabricated, or intercepted data

(c) not make timely decision due to denial of service

(d) pay financial liability for public disclosure of confidential data.

The data value may also include lost revenue from leakage of trade secrets to competitors, and advance use of secret financial data by rogue employees in the stock market prior to public-release.

Identifying Data Vulnerabilities 

This phase requires the identification and documentation of vulnerabilities associated with DW environment. Some common vulnerabilities of DW include the following:

• In-built DBMS security
• DBMS limitations
• Inference attacks
• Availability factor
• Human factor
• Inside threats
• Outsider threats 
• Natural factors 
• Utility factors. 

A comprehensive inventory of vulnerabilities inherent in the DW environment need to be documented and organized (e.g. as major or minor) for the next phase. 

ldentifying Protective Measures and Their Cost

Vulnerabilities identified in the previous phase should be considered in order to determine cost-effective protection for the DW data at different sensitivity levels. Some protective measures for the DW data include:

• Human wall
• Access user classification
• Access controls
• Data encryption
• Partitioning
• Development controls.

The estimated costs of each security measure should be determined and documented for the next phase. Measuring the costs usually involves determining the development, implementation and maintenance costs of each security measure.

Selective Cost-Effective Security Measures

All security measures involve expenses, and security; expenses require justification. This phase relies on the results of previous phases to access the fiscal impact of corporate data at risk and select cost-effective security measures to safeguard the data against known vulnerabilities.

However, the cost factor should not be the only criterion for selecting appropriate security measures in DW environment. Compatibility, adaptability and potential impact on the DW performance should also be taken into consideration.

E­valuating the Effectiveness of Security Measures

A winning basketball formula from the John Wooden School of Thought teaches that good team should be prepared to rebound every shot that goes up, even if it is made by the greatest player on the court. Similarly, a winning security strategy is to assume that all security measures are breakable, or not permanently effective. Every time we identify and select cost-effective security measures to secure our strategic information assets against certain attacks, the attackers tend to double their efforts in identifying methods to defeat our implemented security measures.

The best we can do is to prevent this from happening, make the attacks difficult to carryout, or be prepared to rebound quickly if our assets are attacked. We will not be well-positioned to do any of these if we do not evaluate the effectiveness of security measures on an ongoing basis.

Evaluating the effectiveness of security measures should be conducted continuously to determine whether the measures are:

(i)    Small, simple and straightforward

1. Carefully analyzed, tested and verified
2. Used properly and selectively so that they do not exclude legitimate accesses.
3. Elastic so that they can respond effectively to changing security requirements, and
4. Reasonably efficient in terms of time, memory space, and user-centric activities so that they do not adversely affect the protected computing resources. It is equally important to ensure that the DW end-user understand and embrace the propriety of security measures through an effective security awareness program. The data warehouse administrator (DWA) with the delegated authority from senior management is responsible for ensuring the effectiveness of security measures.

The size of historical data in the DW environment grows significantly every year, while the user of the data tends to decrease dramatically. This increase storage, processing and operating costs of the DW annually. It necessitates the periodic phasing out of least and most accessed data over a long time horizon. A prudent decision has to be made as to how long historical data should be kept in the DW environment before they are phased our on mass. The DWA may not meet effectively these challenges without the necessary tools (activity and data monitors), resources (funds and staffing support) and management philosophy (strategic planning and management). For these reasons, the DWA should be a good strategist, an effective communicator and a competent technician. It is generally recognized that the goal of DW is to provide decision-makers access to consistent, reliable and timely data for analytical, planning and assessment purposes in a format that allows for easy retrieval, exploration and analysis. The need for accurate information in the most efficient and effective manner is congruent with the security requirements for data integrity and availability.