Financial Services

Highly targeted by cyber criminals, today’s banks, insurance companies, investment managers, and other financial services firms have countered with major investments in security, compliance and risk management programs.

As a result, a wide range of deployed defenses protect customers’ Personally Identifiable Information (PII), Payment Card Information (PCI), and other sensitive data, generating a tremendous volume of security data in the process.

Security teams must assess and analyze this constant flow of incident, alert and alarm data, seeking to differentiate between benign and malicious activity.  However, the sheer volume of data and the amount of time required to investigate individual incidents means analysts rarely have sufficient resources to address every issue, creating a growing backlog.

Risk Fabric automates manual processes and pinpoints evidence of hard-to-detect multi-stage attacks that infiltrate financial IT systems, greatly advancing multiple security, compliance and risk management processes.

Media & Telecom

Risk Fabric uses advanced analytics to correlate security and policy data generated across numerous platforms into a context-rich stream of information that can be visualized in targeted, role-based dashboards for immediate use.

Media and telecom conglomerates have grown through mergers and acquisitions of peer organizations and continued expansion of their business by converging news and entertainment across television, film, mobile, and digital networks.

Assets including subscriber data, original content, and talent relationships are valuable targets for attackers both inside and outside the organization.  With IT infrastructures assembled via frequent M&A, security teams are forced to sort through disparate and distributed silos of security data residing on a broad range of cyber and IT systems to understand their environments.

Aggregating and assessing all this security data has traditionally been a largely manual process, which means that any insights age quickly, and practitioners often extract what they can from outdated spreadsheet-based summary reports.


Understanding market demands, fostering constant innovation and delivering groundbreaking products to customers is the mission of today’s manufacturing organizations.

R&D investments and collaborative product development cycles involving design, engineering and manufacturing generate huge volumes of intellectual property to be categorized and protected. In today’s environment, automated assembly lines with industrial control systems can also be targeted by malware and other advanced threats.

This broad attack surface makes it difficult to identify which threats represent the greatest risk, as analysts must compile and analyze data across multiple systems in attempting to visualize their overall security posture.

Risk Fabric identifies and escalates those threats that require immediate investigation and remediation based on cross-referencing of users, entities, threat types and context across every relevant data security source.


Risk Fabric automatically determines the baseline behavior of all users and entities from existing security and IT telemetry data, developing context-rich analysis to isolate and prioritize truly malicious activities for deeper investigation.

Healthcare providers have invested heavily in digital technologies to increase the quality of patient care, while constantly seeking to expand treatment options and reduce costs.

Advancements including electronic health records have also dramatically increased their legal responsibility to ensure privacy of patient PII data, while meeting the requirements of HIPAA and other compliance standards. With black market values for health data higher than social security or credit card numbers, the need to prevent user and entity based threats has only become more intensive.

At the same time, it remains extremely difficult for these security teams to differentiate between normal and risky behaviors across this diverse mix of users and roles; new network-connected entities appear constantly, spanning a broad range of locations, applications and systems.


Federal agencies are clearly subject to a broad array of advanced threats and attacks from the world’s most sophisticated adversaries. Government security teams must focus on protecting agency assets that, if compromised, could cause the most damage to their agencies’ missions.

Existing processes utilized to determine and communicate asset criticality are still highly manual, and typically lack the context provided via correlation of asset parameters with up-to-the-minute cyber and IT infrastructure telemetry.  As a result, advanced behavioral analytics are needed to help prioritize remediation based on greatest impact to agency mission.

Risk Fabric enables agencies to continuously assess, measure, and control the specific mission impact of cyber risks, based on actual conditions detected in their environment, even as cyber threats continue to evolve.


Risk Fabric readily integrates with leading data protection platforms such as Symantec DLP to simplify incident triage, automate remediation and shorten policy development and review cycles.

Developing state-of-the-art innovations to capitalize on emerging and high growth markets remains the lifeblood of today’s technology providers. Creative workforces collaborate globally, with designers, suppliers and manufacturers using a diverse set of on-premise, cloud and mobile infrastructures to execute their work.

Meanwhile, these organizations’ security teams must protect related trade secrets and intellectual property created across numerous applications, including sensitive data being shared via email and other platforms on an ever-changing array of devices.   With frequent use of cloud-based data centers to house business, customer, and confidential data, the overall security picture has become increasingly complex.

These practitioners require data protection strategies that safeguard sensitive information across multiple channels, with consistent policies and integrated analytics to assess and prioritize huge volumes of incident data.

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