NetWitness Platform has an important place in the history of the threat detection and response market. Originally started as a U.S. government research project, the technology has grown and evolved while protecting many of the world’s largest, most complex and most security-conscious organizations. Now a leading SIEM, NDR and OT solution, NetWitness Platform serves as the foundation of cutting-edge security operations centers (SOCs), which rely on its powerful threat detection, incident response and security automation capabilities to battle the stealthiest threats and the most sophisticated adversaries.
For the past decade, NetWitness Platform has been part of the Security product family. We’ve stood shoulder-to-shoulder with our customers as they’ve defended against an ever-increasing and ever-evolving threat landscape, adapting our solutions to contend with new challenges. In addition to its original focus on network analytics, today NetWitness Platform correlates threat data across support for logs, endpoints, and IoT, with advanced behavior analytics and security orchestration & automation. Advanced Cyber Defense and Incident Response services teams continuously apply the technology against real-world attacks from real-world adversaries, generating a feedback loop and driving the continuous improvements that the market demands.
Part of that evolution is cloud support. Traditionally deployed on-premises to support in-house SOCs, NetWitness Platform has long embraced a “see everything, run anywhere” architecture that allows organizations to run any component – from collectors to analytics, as well as incident response and orchestration – in public or private cloud instances. Many organizations take advantage of this virtualization capability today, for reasons ranging from cloud visibility to cost optimization.
What is NetWitness Detect AI?
Today we are announcing the availability of the next evolution in the NetWitness Platform cloud strategy. NetWitness Detect AI is a cloud-native analytics solution that leverages cloud scalability and elasticity, avoiding the need for most hardware while simplifying planning and administration.
Why is this important? It makes the work of security analysts better, faster, and smarter. Running the advanced analytics and machine learning engines from NetWitness Platform as cloud-native services deliver big benefits to security analysts and threat hunters, as well as to administrators and IT resources. NetWitness Detect AI performs cloud-scale detection and correlation, freeing up personnel to focus on defensive activities and giving them the meaningful data and insights they need to accelerate incident resolution.
NetWitness Detect AI’s unsupervised machine learning algorithms work across a wide range of use cases, including detection of insider threats, brute force authentication and machine operated activities. It gets continuously smarter about the specifics of your environment as more data flows through it, and its machine learning algorithms are continuously refined and updated by data scientists, so your analysts don’t have to worry about tuning them. NetWitness Detect AI combines its proprietary machine learning algorithms with an innovative risk scoring model designed to alleviate alert fatigue for analysts by only alerting on high fidelity and high priority threats, leading to faster attack investigation and response times, and driving more efficient and complete incident management.
NetWitness Detect AI can accommodate the processing requirements of organizations large and small. The elastic nature of the cloud means that the service only consumes the resources it requires, without the need to keep capacity in reserve for traffic spikes or incident response activities. Flexible licensing options make planning and budgeting easy, even as situations and use cases change.
As we continue to work closely with existing and prospective customers to improve NetWitness Platform, will always make enhancements to the technology that meet evolving deployment models, use cases, and threat vectors and techniques. NetWitness Detect AI is the first step in leveraging pure cloud SaaS capabilities to benefit the advanced SOC, but you’ll see many more exciting innovations in the coming months and years.
How Does NetWitness Detect AI Work?
NetWitness Detect AI combines data analysis, behavioural baselining, unsupervised machine learning, peer grouping, and risk scoring to identify activity that deviates from normal patterns.
The process works across several stages:
- Ingest security data: NetWitness Detect AI uses network, endpoint, and log data captured by the NetWitness Platform.
- Establish behavioural baselines: Machine learning models learn normal user, entity, and IT activity within the environment.
- Identify anomalies: The system detects deviations from established behavioural patterns that could indicate suspicious activity.
- Add behavioral context: Intelligent peer grouping helps determine whether unusual activity is genuinely anomalous by comparing users and entities with similar behavior.
- Calculate risk: Multiple indicators are combined through a dynamic statistical risk-scoring model.
- Prioritize alerts: Higher-risk activity is surfaced to analysts so they can focus investigation on the threats that require attention.
This approach supports AI-powered threat detection without requiring analysts to manually create and continuously tune every detection model.
How Machine Learning Improves Threat Detection
Traditional detection approaches often depend on known indicators, signatures, or predefined rules. These methods can be effective for known threats but may struggle when attackers use new techniques or behave differently from established patterns.
Machine learning threat detection adds another layer by learning what normal activity looks like and identifying meaningful deviations. NetWitness Detect AI uses unsupervised machine learning, allowing its algorithms to identify abnormal behaviour without requiring predefined labels for every threat.
The models are also continuously refined by NetWitness data scientists, reducing the need for security teams to manually tune the underlying algorithms.
This makes machine learning particularly useful for detecting suspicious behaviours that may not match an existing signature or rule.
Key Capabilities of NetWitness Detect AI
NetWitness Detect AI combines several capabilities to support automated, behaviour-based detection:
- Unsupervised machine learning: Detects abnormal activity without relying exclusively on predefined rules or signatures.
- Behavioural analytics: Establishes baselines for normal user and entity activity and identifies deviations.
- Dynamic risk scoring: Combines multiple indicators to prioritize higher-risk activity.
- Intelligent peer grouping: Compares users and entities with similar behavioural patterns to add context to anomalies.
- Cloud-native scalability: Processes large volumes of security data without requiring additional hardware.
- Automated model refinement: Machine learning algorithms are regularly refined by NetWitness data scientists.
- High-fidelity detection: Focuses analyst attention on higher-priority threats rather than overwhelming teams with every anomaly.
Together, these capabilities make NetWitness Detect AI part of a broader AI cybersecurity approach focused on detecting abnormal behaviour and prioritizing meaningful threats.
What Threats Can Detect AI Help Identify?
NetWitness Detect AI is designed to identify suspicious behaviour across a range of security use cases. These include:
- Insider threats
- Brute-force authentication activity
- Machine-operated or automated activity
- Abnormal user behaviour
- Suspicious endpoint and network behaviour
- Unknown or hard-to-predict threats
- Employee misuse and other anomalous activity
Its behavioural approach is particularly useful when an attack does not match a known signature. By analysing deviations from normal activity, the technology can provide another way to identify potentially malicious behaviour.
How Detect AI Helps Reduce Alert Fatigue
Security teams often have to investigate large volumes of alerts, making it difficult to separate routine anomalies from activity that represents a meaningful security risk.
NetWitness Detect AI addresses this through behavioural analysis and dynamic risk scoring. Rather than treating every unusual event as equally important, it combines indicators of suspicious activity and assigns risk based on the overall behaviour. This helps surface higher-fidelity, higher-priority threats for analyst investigation.
Intelligent peer grouping adds another layer of context. For example, if one employee shows an unusual increase in failed authentication attempts but similar users are experiencing the same behaviour, the anomaly may be less indicative of an attack. Detect AI can use these behavioural comparisons to provide more context around the activity.
The result is a more focused approach to behavioural analytics cybersecurity, where analysts can spend less time sorting through low-value alerts and more time investigating activity that carries a higher level of risk.
Conclusion
NetWitness Detect AI adds a behavioral layer to threat detection by using machine learning to identify activity that falls outside normal patterns. Instead of depending only on known indicators, it looks at how users, entities, endpoints, and network activity behave over time.
For security teams, this means another way to identify suspicious activity, investigate unknown threats, and prioritise events that need attention. By combining AI threat detection, behavioural analytics, and risk scoring, NetWitness Detect AI helps turn large volumes of security data into more relevant signals for analysts.
As attack techniques continue to change, machine learning can help security teams identify behaviour that traditional detection methods may miss. NetWitness Detect AI brings that capability into the broader NetWitness platform, giving analysts additional context for detecting and investigating threats.
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