The worst thing about an inside job is that once it’s detected, it’s usually too late. Early detection is critical to prevent considerable damage arising out of insider threats to the business. But that’s easier said than done! Whether it’s a rogue trader in a bank or brokerage or someone illegally sharing company intellectual property or intelligence, illegal insider actions put enterprises at risk of losing millions. This could be in the form of reputational damage or unfavorable regulatory consequences in case of compromised customer data, for example.
Enterprises prepare to avoid or tackle threats by monitoring employee and third-party communications such as e-mail, instant messaging, social media, and voice, as well as by analyzing log files and file attachments. This conventional approach also employs a Relational Database Management System (RDBMS) technology, which, however, falls short in meeting current business demands for scalable, flexible and cost-efficient solutions to insider threat.
Moreover, this approach struggles to deal with the large volume and variety of data that must be analyzed and often correlated. Analyzing unstructured data sets such as text, audio and images are challenging, especially while determining illegal intent in communications. Worse, insider attacks remain undetected as data is disregarded before it can be correlated, or patterns identified in time.
A tag team against insider threat
Accenture and Cloudera have joined hands to help enterprises move from a post-incident forensic approach to a proactive, preventive method to insider threat. The Insider Threat Detection Solution leverages Cloudera Enterprise together with Accenture’s Aspire Content Processing and Analysis technology and fraud detection consulting expertise to devise a faster, stronger and smarter anti-insider threat system.
The comprehensive solution covers simple text analytics and communications monitoring for e-mail, voice and text messages, to more complex pattern detection and machine learning-based text analysis models. This analysis can also be extended to determine a “risk score” for employee behavior based on application logs, transactions, and other user behavior data. Our joint solution is scalable and can effectively manage the growing volumes of communications data, and extendable with sophisticated risk analysis techniques.
- Quicker: Faster time to incident investigation and response with comprehensive enterprise visibility
- Smarter: Deeper insights with a full complement of analytic and machine learning frameworks for all threat detection
- Extendable: Continued innovation, learning, and application of best practices to insider threat programs
The business benefit of this solution is to enable enterprises to move from a post-incident forensic approach to a proactive preventative approach saving millions of dollars.
Accenture and Cloudera’s Insider Threat Detection Solution features the following components and capabilities:
- Cloudera Enterprise: A scalable and secure data platform, which is machine-learning ready and optimized for the cloud, offers deeper insights with a full complement of analytics frameworks for threat detection.
- Accenture insider threat program development: With Accenture’s vast expertise and experience in fraud and risk analytics, the joint solution brings in continuous innovation and application of insider threat best practices.
- Accenture insider threat detection IP portfolio: Leverage Accenture IP in content processing, natural language understanding, risk analytics modeling, and data visualization to deliver faster incident investigation and response with complete visibility into enterprise data.
Next Step – Engage with us to schedule a discovery session to identify:
- Organizational needs to monitor existing and new data sources
- Requirements for data protection and governance
- Areas where we can bring immediate value to data governance
Contact us at firstname.lastname@example.org or email@example.com
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