Why AI threat detection is reshaping the SOC
- What is AI threat detection?
- Why AI threat detection is essential
- AI threat detection vs. traditional (signature-based and regex) detection methods
- How AI threat detection works
- Use cases and examples
- Challenges and limitations of AI threat detection
- Getting started with AI-driven detection using Corelight
- FAQs
What is AI threat detection?
AI threat detection, also known as AI-driven threat detection, is the use of artificial intelligence (AI) to identify potential cybersecurity threats. These threats may include malware, ransomware, phishing, insider threats, network intrusion, and other malicious activity. Often, AI threat detection uses machine learning (supervised and/or unsupervised) to analyze large volumes of data (for example, network traffic logs, system logs, etc.) for patterns, unusual activity, anomalies, and other possible indicators of compromise (IOCs). AI-driven threat detection differs from traditional methods of threat detection (like signature-based engines, regex patterns, IOC matching, and other rule-based systems) in that AI threat detection can train and learn from new data and behaviors as they evolve. AI threat detection can also detect new and never-before-seen threats, where a signature or pattern does not exist yet, enabling proactive threat hunting.
Why AI threat detection is essential
Organizations today face increasing problems from cybercriminal attacks and pressure to increase efficiency, including in the security operations center (SOC). With attackers using AI more frequently, a growing attack surface (from organizations’ adoption of cloud, IoT, and hybrid work), lower SOC throughput, and an acute shortage of skilled analysts, it’s clear that organizations need the benefits that AI is bringing to the SOC.
Defenders, in turn, must use AI-based tools across their security portfolio to help them proactively hunt for threats, analyze complex data sets, and accelerate and even automate incident response. SOCs can only keep up with attackers by adopting AI in their defenses.
AI threat detection vs. traditional (signature-based and regex) detection methods
AI threat detection uses machine learning, deep learning, and statistical methods. AI threat detection typically trains and learns from large datasets. These datasets can be labeled (as already identified malware and threats) or unlabeled (such as standard network traffic over a period of time). For example, after the models in supervised machine learning have been trained (where labeled datasets are used), AI threat detection can learn to identify similar malware, tactics, techniques, and procedures (TTPs), even if used in a different manner or environment. This differs from traditional signature-based security, where known attacks and attacker mechanisms are used to create signatures, or other traditional security mechanisms that look to match specific patterns or rules (e.g., matching an indicator of compromise, such as an IP address where malware is hosted, using direct matching and/or regex rules).
With traditional security, an exact signature, pattern, or rule match is required, typically resulting in minimal false positives, but it limits the technology to identifying only known, previously used malware and threats. As such, traditional security is typically blind to zero-day threats and requires constant updates of new signatures and rules. Traditional security also cannot identify fileless attacks, like those that rely on existing tools in the environment, also known as living-off-the-land (LOTL).
Unlike traditional signature-based security, AI-based threat detection can identify both known and unknown malware, including zero-day, evolving, and insider threats. Identifying known threats is also important to help identify known threats that traditional detection methods may have missed, in addition to the new, unknown threats that AI threat detection has the unique capability of detecting. Because AI threat detection learns as new data is acquired, it can adapt to changing TTPs and be useful against evolving threats, insider threats, and sophisticated APTs. With AI threat detection, there may be a higher risk of false positives if the models are poorly tuned or the data used to train them is of low quality. It’s important to use the best data and telemetry in training to avoid bias or blind spots. AI threat detection is also typically much more resource-intensive than traditional security methods.
AI threat detection and traditional signature-based detection are two major categories in threat detection. Anomaly detection, on the other hand, is only a single type of detection under the umbrella of AI threat detection. While AI threat detection can be used for a number of types of detections, anomaly detection specifically uses unsupervised machine learning over a learning period to analyze larger and more comprehensive data sets to establish a baseline or normal behavior in their networks and other environments. Once training has been completed, anomaly detection is used to detect and identify unusual and anomalous behavior. With anomaly detection, SOC analysts can more easily identify zero-day, evasive, and novel attacks as part of a strategic multi-layered detection strategy that incorporates traditional and AI-based threat detection capabilities.
How AI threat detection works
AI threat detection follows a number of stages and steps.
Techniques | Details |
---|---|
Data Collection |
The first stage is Data Collection. The data collection stage is perhaps the most important, as the source and quality of the data used will determine the effectiveness of the AI threat detection. There are two types of data used in AI threat detection: data used to train the AI models (typically provided by the AI threat detection vendor and used in the first stage), and data used in the detection phase (data provided by the end SecOps team used during Detection and Alerting). Poor data in either phase will result in biased, skewed, and missed detections. For AI model training, it is important to use a rich data source that is non-biased, and if using a vendor, find one that does not use live customer data for privacy and compliance reasons. For the detection and alerting phase, a comprehensive, rich source of data will enable the best results from AI threat detection. The data used could be from logs, network traffic, endpoint activity, cloud telemetry, email activity, web activity, and other sources. This data typically includes information around user logins, process executions, file changes, data transfers, and more. |
Feature Extraction |
The second stage is Feature Extraction. In this stage, the raw data is analyzed, and meaningful indicators are extracted. These indicators can include metadata such as login time, source and destination IP addresses, file hashes, executed commands, usernames, user agents, and other possible identifiers. These extracted features are then used as the inputs for the machine learning (ML) models. |
Model training |
The third stage is Model Training. The actual model training will differ depending on the type of AI being used. Different AI techniques that can be used for threat detection include supervised ML, unsupervised ML, and deep learning. |
Baseline Creation |
In supervised ML, data has to be labeled and identified as either malicious or benign. The more data used to train the model, the more effective the AI threat detection. In unsupervised ML, an observation period, known as Baseline Creation, has to occur for it to learn what normal activity looks like. This activity can include user, host, app, and network behaviors. Deep learning is used to model complex patterns, for example, the recognition of polymorphic malware behavior. |
Detection and Alerting |
The fourth stage is Detection and Alerting. AI compares real-time activity against the models and baselines. For anomaly detection, if activity deviates significantly from the determined baseline, it can trigger an alert or automated responses. |
Continuous Learning |
The fifth and final stage is Continuous Learning. The AI model updates as new threats and patterns emerge, and feedback from SOC analysts can retrain or fine-tune the system. AI threat detection works by learning from data, spotting anomalies, and identifying malicious behavior. This makes it more adaptive than rule-based systems and is one of the reasons AI threat detection is useful for proactive threat detection. |
Use cases and examples
AI threat detection excels at several use cases across the IT security framework. While its capabilities are rapidly increasing, organizations still need to deploy it alongside traditional threat detection. Traditional security provides efficiency for detecting known attacks, while AI/ML is proficient in detecting new, stealthy, and unknown attacks. A multi-layered threat detection architecture provides the best balance of speed, accuracy, and adaptability.
Organizations should consider implementing AI threat detection for several critical use cases. The rapidly evolving landscape of cyber threats, characterized by increasingly sophisticated and automated attacks, necessitates a proactive and intelligent approach to security. AI-powered solutions offer the ability to analyze vast quantities of data in real-time, identify anomalous behaviors that might indicate malicious activity, and respond with greater speed and accuracy than traditional, signature-based methods. This is particularly crucial for detecting novel and polymorphic threats that evade conventional defenses.
Key use cases for AI threat detection currently include:
Real-time anomaly detection. AI models can establish baselines of normal network, user, and entity behavior. Any significant deviation from these baselines, such as unusual login and activity times, data access patterns, or sudden spikes in network traffic, can trigger an alert, indicating potential compromise, insider, or other threat activity. This is vital for identifying zero-day and living-off-the-land (LOTL) attacks that have no known signatures.
Malware and ransomware detection. While traditional threat detection software relies on known signatures, AI can analyze file characteristics, execution behavior, and system interactions to identify new and polymorphic malware strains (including ransomware), even if they haven't been seen before. This behavioral analysis is a powerful defense against evolving threats.
Phishing and social engineering prevention: AI algorithms can analyze email headers, content, sender reputation, and embedded links to identify sophisticated phishing attempts, including spear-phishing and business email compromise (BEC) attacks, which often bypass basic spam filters.
Insider threat detection. AI can monitor user activities across various systems and applications to detect unusual activity and suspicious patterns that might indicate an insider attempting to exfiltrate data, abuse privileges, or sabotage systems. This includes unusual data access, large file transfers, or attempts to bypass security controls.
Cloud security monitoring. With more organizations moving to cloud environments, AI is essential for monitoring distributed cloud infrastructures for misconfigurations, unauthorized access, and malicious activity. It can analyze logs and traffic across multiple cloud services to provide a consolidated view of potential threats.
Vulnerability management and patch prioritization. AI can analyze threat intelligence, vulnerability databases, and an organization's asset inventory to identify critical vulnerabilities and prioritize patching efforts based on the likelihood and potential impact of exploitation.
Incident response automation. AI can automate parts of the incident response process, such as correlating alerts, enriching threat intelligence, providing simple-to-understand explanations of alerts, offering triage suggestions, and initiating automated remediation actions (e.g., isolating compromised endpoints, blocking malicious IPs), thereby reducing response times and minimizing damage.
Network intrusion detection. AI can detect abnormal traffic patterns that indicate attacks. For example, AI can analyze NetFlow and DNS traffic to spot command and control (C2) communications, even if the attacker rotates IP addresses.
Zero-day attack detection. AI can spot previously unknown exploits before patches exist. For example, AI models notice a spike in abnormal process behavior or privilege escalations on multiple machines - even though no signature exists for the exploit.
Fraud and account takeover protection. AI can identify fraudulent logins or payments in real-time. For example, a banking user who typically logs in from California on a Windows laptop suddenly logs in from Russia using an Android device at 3 a.m. AI can flag and block the transaction.
Implementing AI threat detection is no longer a luxury but a strategic imperative for organizations aiming to build robust, future-proof cybersecurity defenses. It empowers security teams to move from a reactive to a proactive stance, significantly enhancing their ability to protect critical assets and maintain operational continuity in the face of persistent and sophisticated cyber threats.
Challenges and limitations of AI threat detection
While AI threat detection has become a powerful and useful tool in the cybersecurity defense arsenal, there are still some challenges and limitations. These include:
- False positives and false negatives: If models are poorly tuned or the data used for training and baselining is of low quality or incomplete, AI threat detection may be more likely to generate false positives and/or miss threats, creating false negatives.
- Data quality and completeness: Obtaining diverse, high-quality, and complete security data is essential. Poor data can skew results, lead to biased detections, or result in missed threats. For anomaly detection, the observation period needs to be long enough to capture all typical activity (for example, a seven-day observation period may miss a monthly event and mark it as an anomaly when, in fact, it's normal activity for the environment). Data collection objects must also be situated to observe all relevant activity. For supervised machine learning, labeled data must be high-quality, vetted, and properly categorized to avoid miscategorized data that can increase false positives and missed detections.
- Resource intensiveness: AI threat detection is typically much more resource-intensive than traditional security methods, and some detections may take longer than traditional methods that are often quicker at detecting known threats with existing signatures.
- Evasion by adversarial attacks: Attackers are leveraging AI more frequently and developing TTPs to evade AI models. While defenders are using AI to secure their operations, attackers are also taking advantage of AI to expand their attack surface and evade AI threat detection. Attackers could also manipulate the input data for AI to fool AI systems.
- Continuous learning and retraining: AI models need to be updated and potentially retrained as new threats and patterns emerge, and they should also be retrained using feedback from SOC analysts to avoid drift and maintain effectiveness.
- Integration with traditional security: While powerful, AI threat detection should be deployed alongside traditional threat detection methods. Traditional security is efficient for known attacks, while AI/ML excels at new, stealthy, and unknown attacks. A multi-layered architecture provides the best balance. This deployment and integration may come with operational challenges due to skill gaps and resource availability.
- Data privacy: Some organizations may have an issue with data privacy and compliance as AI collects and analyzes potentially sensitive logs, network traffic, endpoint data, and other information.
- Cost and maintenance: AI threat detection has ongoing maintenance and costs, as models must be retrained regularly. There is also an operational cost to maintaining AI systems due to their resource intensity and the cost of training staff to use AI-based systems.
- Human dependency: AI can assist with threat detection, but it is not a silver bullet, and currently can not fully replace human threat hunters and analysts. AI may miss business-specific nuances that only a human would understand.
AI threat detection can improve the speed and scale of a SOC, but it can also have issues with false positives, explainability, and integration challenges. AI works best today when combined with traditional detection methods as well as human intelligence and experience.
Getting started with AI-driven detection using Corelight
Corelight Open NDR (Network Detection and Response) Platform gives you unmatched network visibility and precision-crafted detections that catch what EDR misses. Backed by AI and automation, you move from alert to action faster. Corelight is a leader in the development and implementation of AI-powered platforms that give defenders the tools for defense-in-depth without compromising company data. Our Open NDR Platform leverages powerful machine learning and open source technologies that detect a wide range of sophisticated attacks and provide analysts with context to interpret security alerts, including LLMs such as ChatGPT. Our approach delivers significant contextual insights while maintaining customer privacy: No proprietary data is sent to LLMs without the customer's knowledge and authorization.
Corelight’s use of AI spans the three primary use cases for AI in cybersecurity: AI-driven threat detection, AI-powered workflows, and the AI-enabled ecosystem. These use cases are all backed by forensic-grade network evidence—plus context, gathered in real-time across on-premise, hybrid, and multi-cloud environments.
Our use of Zeek® and Suricata®, as well as partnerships with CrowdStrike, Microsoft Security, and other security consortia, delivers the double benefit of maximized visibility and high-quality contextual evidence that has helped us expand our offerings of supervised, unsupervised, and deep learning models for threat detection.
Corelight's AI/ML-powered threat detection leverages a multi-layered engine and the industry's most comprehensive evidence. It employs a variety of supervised and unsupervised machine learning models, including RF, CNN, RNN, NCF, and clustering, to select the most effective tool for each task. Corelight threat detection capabilities are available on both sensors and in the cloud, and can be customized to suit individual customer environments. It's important to note that Corelight ML models are never trained using customer data.
Corelight offers the GenAI Accelerator Pack to seamlessly integrate with the organization's ecosystem. The pack includes a Model Context Protocol (MCP) Server, Analyst Assistant Promptbooks, and Investigation Promptbooks. It combines industry-standard network evidence with the power of large language models (LLMs) to accelerate and enhance security operations center (SOC) workflows.
At Corelight, we're committed to transparency and responsible stewardship of data, privacy, and AI model development. We help analysts automate workflows, improve detections, and expand investigations via new, powerful context and insights. We encourage you to keep current with how our solutions are optimizing SOC efficiency, accelerating response, upleveling analysts, and helping to mitigate staffing shortages and skill gaps.
Corelight's Open NDR Platform provides a powerful foundation for proactive threat hunting by combining rich network evidence, open-source intelligence, and integration with other security tools, enabling security teams to proactively detect, investigate, and respond to threats more effectively.
Frequently asked questions
Anomaly detection is perhaps the most well-known type of threat detection using AI. But anomaly detection is just one possible type of detection available under the umbrella of AI threat detection. Typically, anomaly detection uses unsupervised machine learning to look for deviations from a defined baseline (what is considered normal) of system, network, or entity behavior. Other types of AI threat detection can use deep learning, natural language processing, supervised or unsupervised machine learning. For example, behavior detection can use labeled data (data that has been identified), to train an AI detection engine to learn patterns and behavior around a specific type of attack, and to look for matching behavior in the environment for signs of an attack, even when other elements of a possible attack have been modified (like originating source, actual malware used, etc.)
In order to get the best results from AI threat detection, it’s essential to have diverse, high-quality security data sources. Poor data can skew results and cause false positives. It’s also important to have complete data. For example, when using AI threat detection for anomaly detection, it is important to observe an environment where activity is typical for an extended period of time. If for example, there are events that only occur monthly on a network, it’s important that the training period for the AI covers a period of time greater than a month to capture the monthly activity, and it’s important for the object (or objects) collecting the data to be situated in a location on the network capable of observing all of the relevant activity.
Likewise, if supervised machine learning (where labeled datasets are used for training) is used for AI threat detection, the labeled data should reflect high-quality, vetted, and properly categorized data. Any miscategorized data can increase false positives and missed detections.
Sources of data that can be used for AI threat detection can include:
- Network and traffic data
- Endpoint and host data
- User and identity data
- Threat intelligence feeds
- Application and cloud data
- Security tools and alert data
In order to train an effective AI threat detection model, high-quality data sources from comprehensive data sources are required, including accurately classified examples in the case of training on labeled data.
AI threat detection can help to reduce overall false positives, especially in a multi-layered threat detection architecture.
Traditional detection can generate false positives since signature-based detection will alert on any match, even if it is benign. Threshold-based detection can also flag harmless behavior (for example, multiple accidental login failures). Even the use of AI threat detection can generate false positives, when the quality of the data being fed to AI is poor or biased. These false positives can overwhelm a SOC with additional noise and workload.
sAI threat detection using quality data can help reduce false positives through
- Contextual analysis: AI can be used to detect threats and then correlate a threat across multiple data sources and detection engines, increasing the confidence that the detection is a valid threat.
- Behavioral baselining: A fundamental basis of anomaly detection is using AI to learn what normal traffic and activity look like during a baseline period, with quality data fed during the baseline, helps reduce alerts for infrequent but harmless behavior.
- Adaptive learning: AI can update and learn as the environment evolves, unlike static detection engines. This reduces false positives caused by changes to workflows and other benign changes in the environment.
- Threat prioritization: AI can help assign risk scores to alerts and events, enabling SOC analysts to prioritize higher-scoring alerts.
AI threat detection using the best data will not eliminate all false positives, but it can significantly reduce the volume of meaningless alerts, enabling SOC analysts to focus on real threats.
To measure true AI threat detection performance, specific KPIs that capture both technical accuracy and operational impact are required. These are some of the KPIs that can be tracked to measure performance.
Core detection accuracy KPIs
- True positive rate (detection rate/recall): % of actual threats correctly identified by AI (higher % shows better performance)
- False positive rate: % of benign events incorrectly detected as threats (lower % results in less analyst fatigue)
- False negative rate (miss rate): % of actual threats the AI missed, useful for measuring blind spots
- Precision (positive predictive value): % of truly malicious threats detected
- F1 score: a machine learning evaluation metric that combines precision and recall scores to measure a model's accuracy, used to balance detection accuracy vs. false positives
Operational effectiveness KPIs
- Mean time to detect (MTTD): how quickly the AI detects an incident
- Mean time to respond (MTTR): how quickly analysts can respond after AI raises an incident
- Alert volume per day: total alerts generated (typically segmented by severity), to help track whether AI is flooding the SOC with noise
- Alert-to-incident ratio: how many AI alerts turn into validated incidents to determine AI efficiency
- Automation coverage: % of incidents where AI detection with an automated response was sufficient without human intervention
Business and risk KPIs
- Reduction in successful breaches: compare incidents pre- and post-AI deployment
- Cost per incident: average cost of detection and response pre- and post-AI deployment, and continuing
- Regulatory / compliance alignment: % of alerts where explainability and audit trails are sufficient for compliance needs
- Model drift/degradation rate: how quickly detection accuracy deteriorates over time without retraining
- Analyst productivity: alerts handled per analyst per shift before and after AI implementation
In practice, SOC and security teams often track detection accuracy metrics for model health, efficiency metrics for operational impact, and business risk metrics for leadership reporting.
In an ideal environment, AI models would be continuously retrained as new data enters. But in practice, some organizations cannot tolerate the associated risk of continuous retraining or have a closed environment where continuous retraining is not possible.
In practice, organizations choose one of three retraining cadences:
- Continuous/online learning
- For highly dynamic environments, useful for fast-evolving malware and phishing campaigns.
- Regularly scheduled retraining
- The SOC/security team chooses the frequency of retraining, 1-3 months if there’s a strong data pipeline, 6-12 months in stable environments. Regular retraining ensures the model adapts to new attack tactics and infrastructure changes.
- Event-triggered retraining
- Retrain after a major environment change, a change in false positive or false negative rate, or detection failures.
Any indication of model drift (drop in precision, increase in false positives and negatives, etc.) is a good indicator of a need to retrain. In addition, best practices include automated monitoring and tracking of KPIs, keeping a human in the loop, rolling A/B training (where multiple models are A/B tested before deployment), and a hybrid approach to retraining (regularly scheduled combined with event-triggered retraining).
Deploying AI threat detection in the cloud brings some great advantages, but also introduces some specific challenges. These challenges include:
- Data security and privacy
- With the cloud, there is always concern over sensitive data and privacy. In addition, there may be concerns around data residency and compliance with regulations like GDPR and HIPAA with regard to where data can be stored and processed.
- In addition to increased use of encryption, it may be difficult for AI to analyze encrypted traffic in the cloud.
- Multi-cloud and hybrid environments
- With multi-cloud and hybrid environments, data may be incomplete, or data between sources may be inconsistent.
- Standardization issues between different providers may make it difficult to integrate and fully process data.
- Lateral movement between clouds and between cloud and on-prem may be more difficult to observe, making detection harder.
- Model performance and reliability
- High data volume can lead to issues with scaling to accommodate the incoming data to the AI models.
- Model drift may be more prevalent in the cloud, where workloads are constantly changing, leading to the possibility of more false positives and missed threats (false negatives).
- Integration and deployment complexity
- Integrations between cloud, hybrid, and on-prem systems, including SIEM/SOAR and other platforms that work with AI detections, may be non-trivial.
- API limitations with cloud services, as not all logs are exposed in real-time, which may create blind spots
- Latency between attack and AI detection in a highly elastic cloud environment
- Skills and operational gaps
- Expertise shortages may exist, as many SOC teams lack deep cloud security and AI/ML expertise.
- Explainability may be an issue with black-box AI detections, which issue alerts, but not the explanation and the evidence behind why an alert was triggered.
- Cost and resource management
- Compute costs may be high from training AI models on large-scale cloud data
- Data ingestion costs if there’s a need to move telemetry between regions and/or clouds
- Operational overhead is ongoing from retraining, tuning, and monitoring in the cloud
Adversarial attacks can deliberately manipulate inputs to AI in an attempt to evade detection. There are a number of ways they attempt to evade detection. These include:
- Evasion attacks (inference-time attacks), where attackers attempt to trick AI into misclassifying malicious activity as benign using:
- Perturbation, making tiny, carefully chosen changes to data (changing a few bytes to malware, altering traffic patterns, etc.) to make traffic look normal, while still enacting an attack.
- Feature manipulation, by adding noise or disguising attack features, for example, padding malware with benign code.
- Mimicking legitimate behavior by imitating a normal user or system activity to avoid detection. Attackers can use legitimate tools on the network to enact their attack, referred to as living-off-the-land (LOTL) attacks.
- Poisoning attacks (training-time attacks) are designed to corrupt the AI model during training so that the AI incorrectly detects behavior and fails to detect the attack. There are a number of ways to enact a poisoning attack. These include:
- Label flipping, by inserting malicious samples labeled as benign and benign samples labeled as malicious.
- Backdoor attacks involve training the model to behave normally except when it sees a specific trigger (possibly a byte sequence), at which point it misclassifies the input.
- Data flooding occurs when the training data is flooded with misleading patterns. This can make the model less accurate overall.
- Model extraction and reverse engineering, with the goal of learning the AI model’s boundaries in order to craft an attack that is just on the safe side of the boundary. It uses techniques like:
- Query-based probing, where it sends test inputs to see how the AI responds and then infers the logic used.
- Surrogate models,
- Obfuscation and adversarial examples in the wild
- Polymorphic malware is malware where the code and structure automatically mutates every time it runs, producing endless variants.
- Adversarial perturbations in network traffic are adding junk traffic or changing packet timing so AI classifiers miss any malicious content.
- AI-generated evasion is where attackers use their own generative AI to test and produce attacks that bypass detection.
While AI threat detection can greatly enhance the efficiency of a human SOC analyst, it does not and typically cannot replace a human analyst. Each contributes to better threat detection.
What AI does well
- Scale and speed: AI can process terabytes of logs, network traffic, and endpoint events quickly.
- Pattern recognition: AI can detect subtle anomalies and correlations across large datasets.
- Noise reduction: AI is useful for filtering out repetitive low-level alerts and prioritizing high-risk events.
- Automation: AI can handle routing tasks, like triaging common alerts, sandboxing suspicious files, and flagging known IOCs.
What humans do better
- Context and judgement: A human analyst can understand business context, for example, identifying an alert as legitimate traffic based on business needs.
- Threat hunting: A human analyst can proactively search for novel attack techniques that AI has not seen.
- Creative reasoning: Attackers often innovate in unexpected ways, humans can adapt and hypothesize beyond what AI models were trained for.
- Incident response: AI raises alerts and can automate parts of the triage process, simplify understanding of the threat, and reduce response time, but until AI is proven reliable for triage, humans are still better at determining final containment strategy, communication, and remediation.
- Ethics and compliance: Humans are still necessary to ensure AI decisions align with policies, regulations, and legal constraints.
AI amplifies efficiency by handling repetitive and large-scale analysis. Humans add expertise and intuition to validate, investigate, and respond to attacks. Right now, AI is best used to assist detection, initial triage, prioritization, and recommendations for response as a complement to a human SOC analyst.
Corelight Open NDR gives you unmatched network visibility and precision-crafted detections that catch what EDR misses. Backed by AI and automation, you move from alert to action—faster, using the best network evidence. Elite defenders in the SOC recognize that security alerts can—and will—be missed. They know that an evidence-first strategy is their best opportunity to catch advanced adversaries in the act.
- Because risk thrives in uncertainty, the best defense is evidence.Cyber risk is an inevitable part of any organization's security posture. Uncertainty makes this risk even harder to deal with. That's why the most sophisticated defenders adopt an evidence-based approach to network security. This strategy removes uncertainty so they can make the right decision in critical moments—when an alert comes in, when a major attack is detected, or when they're remediating a breach—so they can deal with risk from the most informed position.
- Complete visibility. Gain a commanding view of your organization and all devices that log onto your network—with access to details such as DNS responses, file hashes, SSL certificate details, and user-agent strings—rapidly, without relying on other teams to respond to data requests.
- Next level analytics. Machine learning—fueled with network evidence—delivers powerful insights so you can focus on the most critical detections. Corelight’s high-fidelity, correlated telemetry powers analytics, machine learning tools, and SOAR playbooks, improving efficiency and unlocking new capabilities so that you can make better decisions—faster.
- Faster investigation. Correlate alerts, evidence, and packets tounderstand network activity and integrate that context directly into your existing workflows. Reduce false positives and your alert backlog—with no redesign or retraining necessary. You get a full view of every incident so you can validate containment and remediation.
- Expert hunting. Rich, organized, and security-specific evidence enables you to spot vulnerabilities, intruder artifacts, critical misconfigurations, signs of compromise, and undetected attacks, further mitigating risk.
Learn more about Corelight Open NDR and its threat detection capabilities.
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