Dark Reading is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Perimeter

12/12/2012
02:50 PM
Adrian Lane
Adrian Lane
Commentary
50%
50%

Don't Get 'Ha-Duped' By Big Data Security Products

Some Big Data security recommendations

A couple of posts back I posed the question, "How do you secure big data environments?" I covered several avenues of attack against NoSQL environments that I considered low-hanging fruit from a security standpoint. To address those attacks, I offer the following recommendations to provide basic cluster security.

But before I make any recommendations to securing NoSQL, I have a couple of requirements for security solutions in big data environments. Many security vendors offer security products for big data, but these are the same products they offer for normal IT environments. Because these solutions were not architected for big data, they often impose limits on how they are deployed, how they scale, or limit big data cluster functionality in some way.

We don't want to be "Ha-Duped" into buying traditional security products that don't work with big data, so all of my recommendations must scale and must not limit the core capabilities of big data. Here is what I recommend:

Kerberos
The goal here is to validate nodes to ensure unwanted copies of data or unwanted queries are run to copy data. In traditional IT, this may seem far-fetched, but in cloud and virtual environments with thousands of nodes in a cluster, this is both easy and difficult to detect. Kerberos provides a means of authenticating nodes before they are allowed to participate on the cluster.

TLS
The goal is to keep communications private. Built-in Hadoop capabilities provide for secure client-to-Web application-layer communication, but not internode communication, nor M-R output before it is passed to the application. TLS provides a mechanism for secure communication between all nodes and services, and scales as nodes are added to the cluster.

File Layer Encryption
The idea is to protect the contents of the cluster from those who administer it. In the event an admin wants to inspect data files, either through a basic text editor or by taking a snapshot and examining the archive, encryption keeps data safe. File-layer encryption is transparent to the database, and scales up as new nodes are added to the cluster. Granted, the encryption keys must be kept secure for this feature to be effective.

Key Management
As an extension of file-layer encryption, key management is critical to successful encryption deployment. All too often when we review cloud and virtual security systems, we find that administrators keep encryption keys stored unprotected on the disk. Encryption keys must be readily available when a cluster restarts; otherwise, data is inaccessible, and leaving keys in the open was the only way they knew how to ensure safe system restarts. Most central key management systems provide for key negotiation on restart, and still keep keys safe.

Deployment Validation
The goal here it to ensure that all nodes in the cluster are properly patched, properly configured, and run the correct (i.e., not hacked) copies of software. It's easy to miss things when you have thousands of nodes running, so automated node validation makes basic node security much easier. Scripts to install patches and set and test configuration settings are an easy way to verify no nodes enter the cluster without proper setup and security. This can also be linked into Kerberos authentication and external application validation (security as a service) offerings.

Logging
Big data is excellent at managing and mining large amounts of data. And most big data environments have logging capabilities baked-in. There is not need to reinvent the wheel: Use these features and the cluster storage model to log events. You can even create security-related M-R scripts to mine the data. There are several commercial and open-source add-ons with additional logging features to help. Note that during this series of posts that I have omitted discussing application security -- specifically, security of the applications that use big data and code vulnerabilities with JSON, Java, and Ruby. I could discuss these as well, given that the security of the big data APIs is of concern. The problems are independent of big data, relevant to any application, and need to be dealt with by application developers with or without a NoSQL database. The subject is beyond the scope of the database, but I raise the issue so that you keep in mind that there are entire classes of attacks that I've not discussed.

Also, note that these are the recommendations I feel comfortable making at this time. There are many different research papers being published on big data security, and I hope to see some innovation in this area within the coming years. And I am starting to see security products architected specifically for NoSQL environments.

Adrian Lane is an analyst/CTO with Securosis LLC, an independent security consulting practice. Special to Dark Reading. Adrian Lane is a Security Strategist and brings over 25 years of industry experience to the Securosis team, much of it at the executive level. Adrian specializes in database security, data security, and secure software development. With experience at Ingres, Oracle, and ... View Full Bio

Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
Data Leak Week: Billions of Sensitive Files Exposed Online
Kelly Jackson Higgins, Executive Editor at Dark Reading,  12/10/2019
Lessons from the NSA: Know Your Assets
Robert Lemos, Contributing Writer,  12/12/2019
4 Tips to Run Fast in the Face of Digital Transformation
Shane Buckley, President & Chief Operating Officer, Gigamon,  12/9/2019
Register for Dark Reading Newsletters
White Papers
Video
Cartoon
Current Issue
The Year in Security: 2019
This Tech Digest provides a wrap up and overview of the year's top cybersecurity news stories. It was a year of new twists on old threats, with fears of another WannaCry-type worm and of a possible botnet army of Wi-Fi routers. But 2019 also underscored the risk of firmware and trusted security tools harboring dangerous holes that cybercriminals and nation-state hackers could readily abuse. Read more.
Flash Poll
Rethinking Enterprise Data Defense
Rethinking Enterprise Data Defense
Frustrated with recurring intrusions and breaches, cybersecurity professionals are questioning some of the industrys conventional wisdom. Heres a look at what theyre thinking about.
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2019-19807
PUBLISHED: 2019-12-15
In the Linux kernel before 5.3.11, sound/core/timer.c has a use-after-free caused by erroneous code refactoring, aka CID-e7af6307a8a5. This is related to snd_timer_open and snd_timer_close_locked. The timeri variable was originally intended to be for a newly created timer instance, but was used for ...
CVE-2014-8650
PUBLISHED: 2019-12-15
python-requests-Kerberos through 0.5 does not handle mutual authentication
CVE-2014-3536
PUBLISHED: 2019-12-15
CFME (CloudForms Management Engine) 5: RHN account information is logged to top_output.log during registration
CVE-2014-3643
PUBLISHED: 2019-12-15
jersey: XXE via parameter entities not disabled by the jersey SAX parser
CVE-2014-3652
PUBLISHED: 2019-12-15
JBoss KeyCloak: Open redirect vulnerability via failure to validate the redirect URL.