What are Detection Accuracy metrics? Crafting the perfect Detection Accuracy metrics can feel overwhelming, particularly when you're juggling daily responsibilities. That's why we've put together a collection of examples to spark your inspiration.
Copy these examples into your preferred app, or you can also use Tability to keep yourself accountable.
Find Detection Accuracy metrics with AI While we have some examples available, it's likely that you'll have specific scenarios that aren't covered here. You can use our free AI metrics generator below to generate your own strategies.
Examples of Detection Accuracy metrics and KPIs 1. Incident Detection Time The time taken from the moment a threat is detected to the initiation of an incident response
What good looks like for this metric: Typically less than 15 minutes
Ideas to improve this metric Implement automated alerting systems Conduct regular threat hunting exercises Enhance staff training on threat identification Integrate with advanced threat intelligence platforms Utilise machine learning for anomaly detection 2. Containment Time The duration between detection and containment of a threat to minimise its spread and impact
What good looks like for this metric: Ideally under 30 minutes
Ideas to improve this metric Automate endpoint isolation procedures Improve network segmentation Establish predefined incident response playbooks Regularly test response strategies Foster collaboration between IT and security teams 3. False Positive Rate The percentage of alerts that are incorrectly identified as threats
What good looks like for this metric: Should be below 5%
Ideas to improve this metric Refine rule sets and detection algorithms Incorporate feedback loops to learn from past alerts Leverage threat intelligence feeds Enhance contextual information in alerts Invest in more precise detection technologies 4. Number of Lateral Movement Attempts Counts of attempts by threats to move laterally within a network after initial access
What good looks like for this metric: Ideally zero attempts
Ideas to improve this metric Deploy micro-segmentation techniques Monitor for unusual access patterns Strengthen user privilege controls Use lateral movement detection tools Conduct regular security audits and penetration testing 5. Incident Recovery Time The time required to fully restore systems and operations post-incident
What good looks like for this metric: Within 24 hours for minor incidents
Ideas to improve this metric Maintain regular backups and restore procedures Invest in resilient infrastructure Document and streamline recovery processes Facilitate cross-department cooperation Regularly update and test recovery plans
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1. Accuracy Proportion of overall correct predictions made by the system
What good looks like for this metric: Typical values range from 85% to 92%
Ideas to improve this metric Regularly update training data with new examples of cyberbullying Employ data augmentation techniques to enhance model robustness Refine algorithms to better differentiate between nuanced bullying and benign interactions Invest in powerful computational resources for training Enhance feature selection to include more relevant variables 2. Precision Proportion of identified bullying incidents that were truly bullying (minimises false positives)
What good looks like for this metric: Typical values range from 80% to 89%
Ideas to improve this metric Implement stricter thresholds for classifying messages as bullying Use ensemble methods to improve precision Incorporate more contextual clues from text data Regularly review and analyse false positive cases Enhance algorithm's sensitivity to language nuances 3. Recall Proportion of actual bullying cases that the system successfully detected (minimises false negatives)
What good looks like for this metric: Typical values range from 86% to 93%
Ideas to improve this metric Increase dataset size with more diverse examples of bullying Utilise semi-supervised learning to leverage unlabelled data Adapt models to recognise emerging slang or code words used in bullying Incorporate real-time updates to improve detection speed Conduct regular system audits to identify and correct blind spots 4. F1-Score Harmonic mean of precision and recall, providing a balanced measure of both
What good looks like for this metric: Typical values range from 83% to 91%
Ideas to improve this metric Focus on improving either precision or recall without sacrificing the other Perform cross-validation to identify optimal model parameters Use advanced NLP techniques for better text understanding Regular user feedback to identify missed detection patterns Continuous deployment for quick implementation of improvements 5. AUC-ROC Measures the ability to distinguish between classes across various thresholds
What good looks like for this metric: Typical values range from 0.89 to 0.95
Ideas to improve this metric Optimise feature selection to improve class separation Apply deep learning methods for better pattern recognition Leverage domain expert input to refine classification criteria Regularly update models to adjust to new trends in digital communication Evaluate model performance using different cut-off points for better discrimination
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Tracking your Detection Accuracy metrics Having a plan is one thing, sticking to it is another.
Don't fall into the set-and-forget trap. It is important to adopt a weekly check-in process to keep your strategy agile – otherwise this is nothing more than a reporting exercise.
A tool like Tability can also help you by combining AI and goal-setting to keep you on track.
More metrics recently published We have more examples to help you below.
Planning resources OKRs are a great way to translate strategies into measurable goals. Here are a list of resources to help you adopt the OKR framework:
Tability's check-ins will save you hours and increase transparency
The best metrics for Threat and Incident Analysis