Welcome Guest. | Log In | Register | Membership Benefits


Topics:   Database Security Tech Center : Security Views

Fraud Detection And DAM

DAM can be used for fraud detection, but you need to review your alerts

Aug 23, 2011 | 01:28 PM | 

By Adrian Lane
Dark Reading

FINRA recently fined Citigroup $500,000 for failing to supervise a sales associate who misappropriated customer funds. From the details provided, it sounds like Citigroup had evidence of the attack and just failed to take notice.

But the point here is not to discuss blame, but clarify some misconceptions about how software is commonly used to detect this type of fraud, and address some of the comments make in Ericka Chickowski article on database controls. There have been notable cases -- such as Global Crossing -- where fraud was detected by database monitoring and auditing, but it requires special considerations on how it is implemented.

1. Database Activity Monitoring platforms don't monitor across databases.

It's not that they can't, it's that they are not usually set up that way. It is difficult to create fraud detection policies because there are so many different ways to commit fraud. And effective policies require cross-database monitoring, which carries a performance penalty due to the way data is stored and policies checked. Note that Citigroup has an effective database activity monitoring platform in place; they have for many years. It monitors intra-database security and compliance checks according to the defined audit, security, and operations policies. But the type of fraud being described cannot commonly be detected with intra-database analysis: multi-database analysis is needed. And it requires several months of transactional data be available in order to check for anomalous transactions.

Inter-database fraud detection requires polices linking specific transaction types together, and to audit stored events over a window of time. Most DAM customers deploy as real-time statement level analysis, not auditing and not to provide referential integrity-checking. Once again, DAM can provide this type of analysis, but there are usually other fraud detection systems in place to detect cross-system anomalies, or customers dump database logs to SIEM systems for correlation and audit reports.

2. Identity is not particularly important with DAM.

That may sound heretical, but the fact is most database queries come over services accounts, and user identity is anonymized at the application layer. Yes, there are many methods to add identity to queries, and tools to attach a federated identity to database access. But a principle use of DAM is to detect malicious queries, so the tool looks for specific attack patterns in the FROM and WHERE clauses. For attribute based detection --- who, which database, time of day, application, etc. -- the user identity is not all that important when you don't care if it is a malicious insider or a hacker who hijacked an account.

You simply need to recognize and stop bad queries regardless of who the actual user is. Customer demand for real-time user identification has historically been low, and is viewed as important forensic investigations, not real-time analysis.

3. Internal audit may not use security tools.

They may use some of the data DAM and SIEM product, and they may even help define the policies for controls and quarterly reports, but they don't actively use databases and security tools. My experience with internal auditors and external auditors from the big four is they use tools they are comfortable with. I most commonly witnessed auditors using Excel spreadsheets and custom macros to root around event data looking for anything weird or unexplained. It's surprising how effective a simple spreadsheet can be for quickly identifying outliers.

Remember, not matter what the tool, it's only as effective as the person who reviews the output. The low-and-slow fraud described by FINRA is difficult to detect, but if you don't diligently review logs and alerts, you're never going to catch it.

Adrian Lane is an analyst/CTO with Securosis LLC, an independent security consulting practice. Special to Dark Reading.



Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

Dark Reading encourages readers to engage in spirited, healthy debate, including taking us to task. However, Dark Reading moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. Dark Reading further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.
Subscribe to RSS



Database Security Reports

report Securing The Data Warehouse
Many enterprises are building data warehouses to centralize the ever-increasing information flowing through their organizations into useful repositories. This makes good business sense, but it opens up a slew of concerns from a security standpoint. IT professionals can apply many of the same security best practices used with databases, but there are new lessons to be learned as well.

report Defend Your Data From Malicious Insiders
The biggest threat to your company?s most sensitive data may be the employee who has legitimate access to corporate databases but less-than-legitimate intentions. And while the incidence of insider data breaches has decreased, external attacks often imitate them--and do serious damage. Follow our advice to mitigate the risk.

report Ensuring Secure Database Access
Role-based access control based on least user privilege is one of the most effective ways to prevent the compromise of corporate data. But proper provisioning is a growing challenging, due to the proliferation of "big data," NoSQLdatabases, and cloud-based data storage.

Other reports from the Database Security Tech Center:

Related Content

Establishing a Strategy for Database Security is No Longer Optional
As databases continue to grow in size, complexity and importance, enterprises struggle to identify the most appropriate controls regarding their use and misuse. The report identifies best practices, including: Implementing database activity monitoring to mitigate the high levels of risk from database vulnerabilities, and address audit findings in areas such as database segregation of duties and change management; using data security measures, such as data masking and data encryption; and monitoring privileged-user access and access to critical data.

Database Activity Monitoring Is Evolving Into Database Audit and Protection
In this report, Gartner writes that "Database audit and protection (DAP) represents an evolutionary advance in database activity monitoring tools." DAP suites provide comprehensive, cross-platform support in heterogeneous database environments to protect sensitive data from inappropriate use. Organizations are increasingly concerned with optimizing database security and mitigating risks associated with database vulnerabilities.

Protecting Against Database Attacks and Insider Threats: Top 5 Scenarios
Data security presents a multi-dimensional challenge in today's complex IT environment. Multiple access paths and permission levels have resulted in a broad array of security threats and vulnerabilities. We invite you to read this new eBook: "Protecting against database attacks and insider threats" to learn the top five scenarios and essential best practices for preventing database attacks and insider threats.

Demo: Distributed Database Security with Real-time Monitoring and Audit Protection
Organizations across the globe continue to experience compromised data caused by malicious attacks, web application vulnerabilities or unauthorized changes. View this demo and learn how IBM InfoSphere Guardium? database activity monitoring can help protect your sensitive data in distributed DBMS environments with a holistic approach to data security and compliance.

Look Beyond Native Database Auditing To Improve Security, Audit Visibility, And Real-Time Protection
Today's attacks on enterprise databases are more sophisticated than ever, and they occur so fast that it's often difficult to stop them in real time. Despite significant efforts to protect enterprise databases, the number of records breached has grown each year - due to all types of internal and external attacks and violations of corporate policy.




Featured Webcasts
Featured Whitepapers
Featured Reports