Use Case

Eliminate False Positives

Proactively identify and solve false positive events using proprietary machine learning to achieve optimal security posture while ensuring business uptime.


Increase security effectiveness and reduce the load on security teams by proactively reducing alert fatigue caused by chasing down false positive events.

Increase effectiveness

Alleviate resources waste by culling false positive events. Focus on actual cyber events, rather than wasting resources on false alarms.

Ensure Business Uptime

Proactively monitor misconfigurations that might cause friction or disrupt business operations and functions.

Reduce risk exposure

Prevent postural gaps posed by turning off a security signature that causes a false positive event

Increase confidence

Re-energize confidence in the security organization by proactively detecting and remediating impacts to applications and users

Unleash the full potential of your security posture

Take action and eliminate false positive alerts across the security stack before the end user opens a ticket, to ensure business uptime and minimize risk exposure.


Machine learning-based analysis

Automatically identify and remediate false positive events based on advanced machine learning algorithms.

Actionable Insights

Make informed decisions based on contextualized data on each alert, including the business context and the potential impact on the organization

One-Click Remediation

Safely remediate security gaps to maximize security. Minimize the risk of false positive events causing unintended consequences through proactive monitoring and granular, pointed exclusions that do not add to organization risk exposure

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False positive events refer to security alerts that are triggered by benign activities, rather than actual security threats. These false alerts can be caused by a variety of factors, such as security misconfigurations or normal network activity that is mistaken for an attack.

How can false positive events be detected?

One approach is to establish baselines of normal behavior for the system or network being monitored, and then compare each alert against these baselines to determine if it is anomalous or not. Another method is to use machine learning algorithms to analyze patterns in the data and identify any outliers that may be indicative of false positives.

How do false positive events impact security operations?

False positive events can have a significant impact on security operations by overwhelming security teams with unnecessary alerts and diverting resources away from actual security threats. This can lead to delayed response times and an increased risk of data breaches.

How can false positive events be reduced or eliminated?

False positive events can be reduced or eliminated by improving the accuracy of security systems, such as through adjusting security rules and configurations, implementing machine learning algorithms, or using threat intelligence feeds.

What is the best approach to minimize the impact of false positive events?

The best approach to minimize the impact of false positive events is through implementing a multi-layered security strategy that includes real-time monitoring, automated analysis and contextualized remediation to quickly identify and resolve false positive events.

Product Overview

Maximize security posture while ensuring business uptime


Connect Veriti with your security solutions

Validate Risk Posture

Identify postural gaps by querying your security configuration

Eliminate False Positives

Reduce alert fatigue. Increase Security Effectiveness

Maintain Cyber Hygiene

Monitor the hygiene of your security solutions

Risk based mitigation

Prioritize and virtually patch vulnerabilities

Enhance zero-day Protection

Identify and distribute zero-day indicators of attack


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