Category: Data Modeling and Analytics

The threat of insiders causing physical harm to fellow employees or themselves at an organization is real. In 2015 and 2016 alone, there were shootings in the U.S. by current or former employees in various workplaces, including at a television station in Virginia, a mowing equipment manufacturer in Kansas, an air force base in Texas, a transportation company in Texas, and a supermarket in Pennsylvania. These incidents resulted in seven fatalities and an additional 17 people injured. Additionally, the December 2015 shooting in San Bernadino, a mixture of workplace violence and radicalization, resulted in 14 deaths and 22 people injured.

According to an FBI report on workplace violence, 80 percent of the active-shooter situations that happened in the United States between 2000 and 2013 took place at work. Of those active-shooter incidents cited in the report, more than 46 percent were perpetrated by employees or former employees and 11 percent involved employees who had been terminated that day. The CERT Insider Threat Center conducted two back-to-back research initiatives to gain a deeper understanding of incidents of workplace violence in the context of insider threat. In this blog post, I describe our most recent research initiative to explore the technical detection of intended harm to self and/or others.

As part of an ongoing effort to keep you informed about our latest work, this blog post summarizes some recently published SEI reports, podcasts, and presentations highlighting our work in cyber warfare, emerging technologies and their risks, domain name system blocking to disrupt malware, best practices in network border protection, robotics, technical debt, and insider threat and workplace violence. These publications highlight the latest work of SEI technologists in these areas. This post includes a listing of each publication, author(s), and links where they can be accessed on the SEI website.

Insider threat continues to be a problem with approximately 50 percent of organizations experiencing at least one malicious insider incident per year, according to the 2017 U.S. State of Cybercrime Survey. Although the attack methods vary depending on the industry, the primary types of attacks identified by researchers at the CERT Insider Threat Center--theft of intellectual property, sabotage, fraud, and espionage--continue to hold true. In our work with public and private industry, we continue to see that insider threats are influenced by a combination of technical, behavioral, and organizational issues. To address these threats, we have published the fifth edition of the Common Sense Guide to Mitigating Insider Threats, which highlights policies, procedures, and technologies to mitigate insider threats in all areas of the organization. In this blog post, excerpted from the latest edition of the guide, I highlight five best practices that are important first steps for an organization interested in establishing a program to implement to protect and detect insider threats.

Many organizations want to share data sets across the enterprise, but taking the first steps can be challenging. These challenges range from purely technical issues, such as data formats and APIs, to organizational cultures in which managers resist sharing data they feel they own. Data Governance is a set of practices that enable data to create value within an enterprise. When launching a data governance initiative, many organizations choose to apply best practices, such as those collected in the Data Management Association's Body of Knowledge (DAMA-BOK). While these practices define a desirable end state, our experience is that attempting to apply them broadly across the enterprise as a first step can be disruptive, expensive, and slow to deliver value. In our work with several industry and government organizations, SEI researchers have developed an incremental approach to launching data governance that delivers immediate payback. This post highlights our approach, which is based on six principles.

Have you ever been developing or acquiring a system and said to yourself, I can't be the first architect to design this type of system. How can I tap into the architecture knowledge that already exists in this domain? If so, you might be looking for a reference architecture. A reference architecture describes a family of similar systems and standardizes nomenclature, defines key solution elements and relationships among them, collects relevant solution patterns, and provides a framework to classify and compare. This blog posting, which is excerpted from the paper, A Reference Architecture for Big Data Systems in the National Security Domain, describes our work developing and applying a reference architecture for big data systems.