As machine and network ecosystems become larger and more complex, they are increasingly targeted for cyber intrusion. Heterogenous infrastructures, vertical and horizontal integrations, reliance on IoT devices, and legacy security solutions all combine to create vulnerabilities. Weaknesses include:
Large attack surfaces provide hackers with an ever-increasing number of potential cyber penetration points. As threat surfaces continue to expand it becomes increasingly difficult to provide consistent levels of security across the entire threat landscape.
Many companies are deploying cyber-security solutions and methodologies that were designed for the threat landscape that existed 20 years ago. While the structure, complexity, and surface area of those ecosystems have continued to evolve, the cyber-security footing has remained static.
Existing security solutions are purely reactive in design. Attacks against machines and networks are often well embedded before an alarm is raised. Asset stakeholders have no ability to predict potential cyber threats or to take preventive action.
that rapidly synthesizes high volumes of structured and unstructured data. AI-enabled data syntheses and live regression analysis closes existing gaps in threat assessment accuracy and provides continuous insights into current and future threats.
that embeds nano-weight machine learning algorithms directly into machines and network equipment, providing asset-centric self-learning and monitoring capabilities. Normal asset behaviors are learned, anomalous behaviors quickly identified, and predictive alerts generated.
via endpoint and edge AL/ML algorithms that provide the active learning, scenario analysis, and virtual triage needed to accurately predict the most effective reactions to potential cyber intrusions.
The transition to a predictive cyber-security state revolutionizes the protection of mission-critical machines and networks. Operations can operate with confidence that their assets and data are much less vulnerable to attack.
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