THE CHALLENGE: Protecting Against Insider Threats
Between January and June 2019, the healthcare industry had already disclosed 285 incidents of patient privacy breaches, with hospital insiders responsible for 20% of the incidents. Similarly, the Verizon 2019 Data Breach Investigations Report, found that 34% of all breaches were caused by insiders.
On average, insider threats cost almost $9 million, take more than two months to contain and include issues related to careless workers, disgruntled employees, workplace violence and malicious insiders.
A recent insider threat study found that 90% of organizations feel vulnerable to insider attacks and 86% have or are building an insider threat program. Still, nearly 75% of C-level executives do not feel they are invested enough to mitigate the risks associated with an insider threat.
That’s why more and more organizations are increasingly using behavior monitoring and similar methods to help with early detection of insider threats. While advanced identity, security intelligence and threat sharing technologies are widely adopted, automation, AI and machine learning are now being used by about 40% of companies. Once investment costs are considered, AI automation could oﬀer the highest net savings of about $2 million and begin to address the shortage in skilled security staﬀ.
Insiders who defraud organizations exhibit consistent risk indicators, and AI can help detect those patterns without the inherent human bias. Additionally, AI can help manage the incredible volume of data that must be collected, aggregated, correlated, analyzed and fused across disparate sources.
Radiance is a deep-web listening tool that uses machine learning and artificial intelligence to assess and prioritize risk.
Radiance scours publicly available data across the entire Internet, correlating names entered into the system with content related to 20 different risk factors, known as Behavioral Affinity Models (BAMs), and cross-referenced with more than 1 million queries into Lumina’s proprietary databases of risk.
Searches provide near-instant results, delivering meaningful, actionable intelligence to identify and prevent risk.
The S4 app, a crowd-sourced, mobile application that allows users to confidentially report concerns in real time.
THE SOLUTION: Leveraging AI to Identify and Prevent Insider Threats
Radiance provides those charged with building an enterprise risk management framework an important tool in predicting risk and mitigating against it, through timely, actionable data derived from deep web listening and analysis.
Radiance includes more than 6,000 terms related to potential national security risks and threats. The platform conducts nearly 120,000 searches across all publicly-available data on the web, correlating names with these terms and cross-referencing over 1 million queries into Lumina’s proprietary databases of risk. Unlike social media monitoring, Radiance is not reliant on a single platform or social media API, allowing for the continuous ingestion of all open source data. Radiance pulls all applicable content into a comprehensive report. The results are prioritized, making it easy to further analyze the findings and determine potential risk. A manual web search of this magnitude would take almost 1 year for one person to complete.
S4 app can be configured as a workplace tool, allowing employees to report insider threats, suspicious activity or concerning behaviors in real time. A centralized management portal allows clients to access real-time threats to geo-fenced facility locations.