Cornerstone concepts to support cybersecurity
While working on a recent project, we came across a newsletter authored by Deb Frincke, then Chief Scientist of Cybersecurity Research for the National Security Division at the Pacific Northwest National Lab in Seattle, which outlined her team’s initiatives for “innovative and proactive science and technology to prevent and counter acts of terror, or malice intended to disrupt the nation’s digital infrastructures.” In cybersecurity, the acknowledged wisdom is that there is no “perfect defense” to prevent a successful cyberattack. Dr. Frincke’s framework defined four cornerstone concepts for architecting effective cybersecurity practices:
- Predictive Defense through use of models, simulations, and behavior analyses to better understand potential threats
- Adaptive Systems that support a scalable, self-defending infrastructure
- Trustworthy Engineering that acknowledges the risks of “weakest links” in complex architecture, the challenges of conflicting stakeholder goals, and the process requirements of sequential buildouts
- Cyber Analytics to provide advanced insights and support for iterative improvement
In this framework, the four cornerstones operate interactively to support a cybersecurity fabric that can address the continuously changing face of cyber threats in today’s world.
If you are a CIO with responsibility for an enterprise data center, you may quickly see that these same cornerstone principles provide an exceptional starting point for planning a resilient data center environment, especially with current generation hybrid architectures. Historically, the IT community has looked at data center reliability through the lens of preventive defense…in the data center, often measured through parameters like 2N, 2N+1, etc redundancy.
However, as the definition of the data center expands beyond the scope of internally managed hardware/software into the integration of modular platforms and cloud services, simple redundancy calculations become only one factor in defining resilience. In this world, Dr. Frincke’s four-part framework provides a valuable starting point for defining a more comprehensive approach to resilience in the modern data center. Let’s look at how these principles can be applied.
Predictive Defense: We believe the starting point for any resilient architecture is comprehensive planning that incorporates modeling (including spatial, CFD, and network traffic) and dynamic utilization simulations for both current and future growth projections to help visualize operations before initiating a project. Current generation software supports extremely rich exploration of data center dynamics to minimize future risks and operational limitations.
Adaptive Systems: Recently, Netflix has earned recognition for its novel use of resilience tools for testing the company’s ability to survive failures and operating abnormalities. The company’s Simian Army, consisting of services (monkeys) that unleash failures on their systems to test how adaptive their environment actually is. These tools, including Chaos Monkey, Janitor Monkey and Conformity Monkey, demonstrate the importance of adaptivity in a world where no team can accurately predict all possible occurrences, and where unanticipated consequence of a failure anywhere in a complex network of hardware fabrics can lead to cascading failures. The data center community needs to challenge itself to find similar means for testing adaptivity in modern hybrid architectures if it is to rise to the challenge of ultrareliability as current scale.
Trustworthy Engineering: Another hallmark of cybersecurity is the understanding that the greatest threats often lie inside the enterprise with disgruntled employees, or simply as a result of human error. Similarly, in modern data center design, tracking a careful path that iteratively builds out the environment while checking off compliance benchmarks and ‘trustworthiness’ at each decision point, becomes a critical step in avoiding the creation of a hybrid house-of-cards.
Analytics: With data center infrastructure management (DCIM) tools becoming more sophisticated, and with advancing integration between facilities measurement and IT systems measurement platforms, the availability of robust data for informing ongoing decision-making in the data center is now possible. No longer is resilient data center architecture just about the building and infrastructure. So, operating by ‘feel’ or ‘experience’ is inadequate. Big data now really must be part of the data center management protocol.
By leveraging these four cornerstone concepts, we believe IT management can begin to frame a more complete, and by extension, robust plan for resiliency when developing data center architectures that bridge the wide array of deployment options in use today. This introduction provides a starting point for ways to use the framework, but we believe that further exploration by data center teams from various industries will create a richer pool of data and ideas that can advance the process for all teams.
REFERENCES
Frincke, Deborah, “I4 Newsletter”, Pacific Northwest National Laboratory, Spring-Summer 2009.