Securosis

Research

Increasing the Cost of Compromise

It seems to be all threat intelligence all the time in the tech media, so I might as well jump on the bandwagon. My pals Wendy Nather of 451 and Jamie Blasco of AlienVault recently did a webcast on the topic. Dan Raywood has a good overview of the content. Wendy does the analyst thing and categorizes the different types of threat intelligence. She points out that sharing is taking place, but more slowly than it should. Jamie then makes a compelling case for why everyone should share threat intel when possible. Shared intelligence increases the cost of compromise. …by removing the secretive aspect, (i.e vendors keeping their threat intelligence close to their chests and monetising it – instead of making it freely available) we can force attackers to raise the bar and spend more and more money on their infrastructure, which decreases the return on investment for cyber criminals. Attackers make crazy money leveraging their tactics. They can buy an inexpensive attack kit (with Bitcoins) and use it a zillion times. If you aren’t talking to your buddy, you don’t know what to look for. If you don’t have a list of C&C nodes or patterns of exfiltration, then when they hit you it won’t immediately raise an alarm. And you will lose. By sharing information we can force attackers to change their attacks more frequently. They will need to turn over botnet nodes faster. Let’s cost them more to do business. Can we make enough difference for them to give up and stop attacking? NFW. They will still make a ton of coin, but over a long enough period this kind of information sharing can get rid of less sophisticated attackers who would make more money doing something legit – you know, like gaming search engine results. Photo credit: “Cento’s Prices (Awesome sign)” originally uploaded by Dave Fayram Share:

Share:
Read Post

Trends In Data Centric Security: Use Cases

After a short hiatus we are back with the next installment of our Data Centric Security series. This post will discuss why customers are interested in this approach, and specific use cases they are looking to address. It should be no surprise that all these use cases are driven by security or compliance. What’s interesting is why other tools and technologies do not meet their needs. What prompts people to look for a different approach to data security? Those are the questions we will address with today’s post. NoSQL / Big Data Security The single biggest reason we are asked about data centric security models is “Big Data”: moving information into NoSQL analytics clusters. Big data systems are simply a new type of database that facilitates fast analysis and lookup capabilities on much larger data sets – at a dramatically lower cost – than previously possible. To get the most out of these databases, lots of data is collected from dozens of sources. The problem is that many sources fall under one or more regulatory controls and contain sensitive data, but big data projects are typically started outside regulatory or IT guidance. As the custodians become aware of their responsibility for the NoSQL data and services, they realize they are unable to adequately secure the cluster – or even know exactly what it contains. To aggravate the problem, reporting and data controls within NoSQL databases are often deficient or completely unavailable. But NoSQL databases have proven their value, and offer previously unavailable scale for analytics, meaning genuine value to the organization. Unfortunately they are often too immature for enterprises to fully trust. Data centric security provides critical security for systems which process sensitive data but cannot themselves be fully trusted, so this approach is very attractive for either protecting data before moving it into a big data repository or transforming existing data into something non-sensitive which can be analyzed but does not need to be secured. The term for this process is “data de-identification”. Examples include substitution of an individual’s Social Security Number with a random number that could be an SSN, or a person’s name with a name randomly chosen or assembled from a directory, or a date with a random proximate date. In this way the original sensitive data is removed entirely, but the value of the data set is retained for analysis. We will detail how later in this series. Cloud and Data Governance Most countries have laws on how citizen data must be secured, outlining custodial responsibilities for companies which store and manage it. These laws differ on which data must be secured, which controls are acceptable, and what is required in case of a breach of sensitive data. If your IT systems are all within a single data center, in a single location under your control, you only need worry about your local laws. But cloud computing make compliance much more complex, especially in public clouds. First, cloud service providers are legally third parties, with deliberately opaque controls and limited access for tenants (customers like you). Second, for reliability and performance many cloud data centers are located in multiple geographic locations, with different laws. This means multiple – possibly conflicting – regulations apply to sensitive data, and you share responsibility with your cloud service providers. The legal issues break down into three type: functional, jurisdictional, and contractual. Functional issues include how legal discovery is performed, what happens in the event of a subpoena or legal hold, proof of data guardianship, and legal seizure in multi-tenant environments. Jurisdictional issues require you to understand applicable legislation, under what circumstances the law applies, and how legal processes differ. Contractual issues cover access to data, data lifecycle management, audit rights, contract termination, and a whole heap of other issues including security and vulnerability management. Data governance and legal issues require substantial research and knowledge to implement polices, often at great expense. Many firms want to leverage low-cost, on-demand cloud computing resources, but hesitate at the huge burden of data governance in and across cloud providers. This is a case where data centric security can reduce compliance burdens and resolve many legal issues. This typically means fewer reports, fewer controls, and less complexity to manage. PHI Queries on how to address HIPAA and Protected Health Information (PHI) were almost non-existent a couple years ago, but we are now asked with increasing frequency. Health care data encompasses many different kinds of sensitive data, and the surrounding issues are complex. A patient’s name is sensitive data in some contexts. Medical history, medications, age, and just about every other piece of data is critical to some audiences, but too sensitive to shared with others. Some patients’ data can be shared in certain limited cases, but not in others. And there many audiences for PHI: state and federal governments, hospitals, insurance companies, employers, organizations conducting clinical trials, pharmaceutical companies, and many more. Each audience has its own relevant data subset and restrictions on access. Data centric security is in use today, providing carefully selected subsets of the complete original data to different audiences, and surrogate data for elements which are required but not permitted. As data storage and management systems become cheaper, faster, and more powerful, providing a unique subset to each audience has become feasible. Each recipient can securely access its own copy, containing only its permitted data. Data centric security enables organizations to provide just those data elements which partners need, without exposing data they cannot access. And this can all be done in real time on demand, by applying appropriate controls to transform the original data into the secured subset. Many tools and techniques developed over the last several years for test data management are now employed to generate custom data sets for individual partners on an ongoing basis. Payment Card Security Tokenization for credit card security was the first data centric security approach to be widely accepted. Hundreds of thousands of organizations replace credit card numbers with data surrogates. Some

Share:
Read Post

Totally Transparent Research is the embodiment of how we work at Securosis. It’s our core operating philosophy, our research policy, and a specific process. We initially developed it to help maintain objectivity while producing licensed research, but its benefits extend to all aspects of our business.

Going beyond Open Source Research, and a far cry from the traditional syndicated research model, we think it’s the best way to produce independent, objective, quality research.

Here’s how it works:

  • Content is developed ‘live’ on the blog. Primary research is generally released in pieces, as a series of posts, so we can digest and integrate feedback, making the end results much stronger than traditional “ivory tower” research.
  • Comments are enabled for posts. All comments are kept except for spam, personal insults of a clearly inflammatory nature, and completely off-topic content that distracts from the discussion. We welcome comments critical of the work, even if somewhat insulting to the authors. Really.
  • Anyone can comment, and no registration is required. Vendors or consultants with a relevant product or offering must properly identify themselves. While their comments won’t be deleted, the writer/moderator will “call out”, identify, and possibly ridicule vendors who fail to do so.
  • Vendors considering licensing the content are welcome to provide feedback, but it must be posted in the comments - just like everyone else. There is no back channel influence on the research findings or posts.
    Analysts must reply to comments and defend the research position, or agree to modify the content.
  • At the end of the post series, the analyst compiles the posts into a paper, presentation, or other delivery vehicle. Public comments/input factors into the research, where appropriate.
  • If the research is distributed as a paper, significant commenters/contributors are acknowledged in the opening of the report. If they did not post their real names, handles used for comments are listed. Commenters do not retain any rights to the report, but their contributions will be recognized.
  • All primary research will be released under a Creative Commons license. The current license is Non-Commercial, Attribution. The analyst, at their discretion, may add a Derivative Works or Share Alike condition.
  • Securosis primary research does not discuss specific vendors or specific products/offerings, unless used to provide context, contrast or to make a point (which is very very rare).
    Although quotes from published primary research (and published primary research only) may be used in press releases, said quotes may never mention a specific vendor, even if the vendor is mentioned in the source report. Securosis must approve any quote to appear in any vendor marketing collateral.
  • Final primary research will be posted on the blog with open comments.
  • Research will be updated periodically to reflect market realities, based on the discretion of the primary analyst. Updated research will be dated and given a version number.
    For research that cannot be developed using this model, such as complex principles or models that are unsuited for a series of blog posts, the content will be chunked up and posted at or before release of the paper to solicit public feedback, and provide an open venue for comments and criticisms.
  • In rare cases Securosis may write papers outside of the primary research agenda, but only if the end result can be non-biased and valuable to the user community to supplement industry-wide efforts or advances. A “Radically Transparent Research” process will be followed in developing these papers, where absolutely all materials are public at all stages of development, including communications (email, call notes).
    Only the free primary research released on our site can be licensed. We will not accept licensing fees on research we charge users to access.
  • All licensed research will be clearly labeled with the licensees. No licensed research will be released without indicating the sources of licensing fees. Again, there will be no back channel influence. We’re open and transparent about our revenue sources.

In essence, we develop all of our research out in the open, and not only seek public comments, but keep those comments indefinitely as a record of the research creation process. If you believe we are biased or not doing our homework, you can call us out on it and it will be there in the record. Our philosophy involves cracking open the research process, and using our readers to eliminate bias and enhance the quality of the work.

On the back end, here’s how we handle this approach with licensees:

  • Licensees may propose paper topics. The topic may be accepted if it is consistent with the Securosis research agenda and goals, but only if it can be covered without bias and will be valuable to the end user community.
  • Analysts produce research according to their own research agendas, and may offer licensing under the same objectivity requirements.
  • The potential licensee will be provided an outline of our research positions and the potential research product so they can determine if it is likely to meet their objectives.
  • Once the licensee agrees, development of the primary research content begins, following the Totally Transparent Research process as outlined above. At this point, there is no money exchanged.
  • Upon completion of the paper, the licensee will receive a release candidate to determine whether the final result still meets their needs.
  • If the content does not meet their needs, the licensee is not required to pay, and the research will be released without licensing or with alternate licensees.
  • Licensees may host and reuse the content for the length of the license (typically one year). This includes placing the content behind a registration process, posting on white paper networks, or translation into other languages. The research will always be hosted at Securosis for free without registration.

Here is the language we currently place in our research project agreements:

Content will be created independently of LICENSEE with no obligations for payment. Once content is complete, LICENSEE will have a 3 day review period to determine if the content meets corporate objectives. If the content is unsuitable, LICENSEE will not be obligated for any payment and Securosis is free to distribute the whitepaper without branding or with alternate licensees, and will not complete any associated webcasts for the declining LICENSEE. Content licensing, webcasts and payment are contingent on the content being acceptable to LICENSEE. This maintains objectivity while limiting the risk to LICENSEE. Securosis maintains all rights to the content and to include Securosis branding in addition to any licensee branding.

Even this process itself is open to criticism. If you have questions or comments, you can email us or comment on the blog.