What is Cisco Secure Network Analytics (Stealthwatch)? How It Uses AI in Network Security
- Cisco Secure Network Analytics is an AI-powered security platform.
- Uses machine learning to detect network anomalies and threats.
- Monitors network traffic across cloud, hybrid, and on-premises environments.
- Provides automated incident response and threat prioritization.
- Uses encrypted traffic analytics (ETA) for malware detection.
- Ensures compliance with security policies and regulatory standards.
What is Cisco Secure Network Analytics (Stealthwatch)? How It Uses AI in Network Security
Cyber threats continue to evolve, requiring businesses to adopt AI-driven security solutions that provide real-time monitoring and response. Cisco Secure Network Analytics (formerly Stealthwatch) is an AI-powered network security platform that helps organizations detect and respond to threats through advanced traffic analysis and anomaly detection.
Cisco Secure Network Analytics enhances network visibility, identifies potential security risks, ensures compliance with security policies, and automates threat mitigation by leveraging artificial intelligence.
With increasing cyberattack concerns, businesses must secure their on-premises, cloud, and hybrid environments. Cisco Secure Network Analytics provides an AI-powered, behavior-based security approach that ensures networks remain resilient against evolving threats, including insider attacks, ransomware, and advanced persistent threats (APTs).
What is Cisco Secure Network Analytics (Stealthwatch)?
Cisco Secure Network Analytics (Stealthwatch) is a network traffic monitoring and security intelligence solution designed to help businesses analyze, detect, and respond to cyber threats in real-time. It provides deep visibility into network activity, using AI-driven analytics to detect anomalies, insider threats, and unauthorized access attempts.
By continuously monitoring data flows and applying machine learning, Stealthwatch helps security teams proactively identify and mitigate security incidents before they cause damage.
Read Top 10 list of AI tools for Network Security and Monitoring.
Key Features of Cisco Secure Network Analytics in Network Security
- AI-Driven Threat Detection – Uses machine learning to identify abnormal network behavior and stop attacks.
- Network Visibility Across Cloud and On-Premises – Monitors traffic across hybrid environments and detects security anomalies.
- Automated Incident Response – AI correlates security events, prioritizes threats, and suggests responses.
- Encrypted Traffic Analytics (ETA) – Detects malware and suspicious activity without decrypting sensitive data.
- Compliance and Risk Assessment – Ensures security policies align with industry regulations and audits.
- Behavioral Analytics – Learns normal user and device behavior to detect deviations and potential threats.
- Integration with Cisco Security Solutions: It works seamlessly with Cisco Talos, Umbrella, SecureX, and other security tools.
- Threat Intelligence Sharing – Uses AI to enhance real-time threat intelligence and detect zero-day vulnerabilities.
- Automated Policy Enforcement – Ensures network access policies are followed, reducing human error.
- User and Entity Behavior Analytics (UEBA) – Identifies unusual activities and flags insider threats.
How Cisco Secure Network Analytics Uses AI in Network Security
Cisco Secure Network Analytics leverages artificial intelligence, machine learning, and behavioral analytics to protect enterprise networks from cyber threats.
Below are the key ways AI enhances its network security capabilities:
1. AI-Powered Network Threat Detection
Stealthwatch’s AI continuously monitors network traffic, identifying unusual patterns that may indicate security threats. It then automatically mitigates risks.
Example: A financial institution detects an unauthorized attempt to access internal databases. Stealthwatch flags the activity as suspicious, allowing security teams to investigate and prevent a potential data breach.
2. Behavioral Analytics for Anomaly Detection
Stealthwatch uses machine learning to understand normal user and device behavior, making detecting anomalies that may indicate cyberattacks or insider threats easier.
Example: If an employee suddenly downloads large volumes of data outside normal work hours, Stealthwatch flags the behavior for review, preventing a possible insider data theft.
3. AI-Driven Encrypted Traffic Analytics (ETA)
Stealthwatch uses AI to analyze encrypted traffic for security threats without decrypting sensitive data, ensuring privacy while detecting malware.
Example: A healthcare provider uses ETA to identify ransomware activity hidden within encrypted data traffic without violating patient confidentiality laws.
4. Automated Incident Response and Risk Prioritization
AI automates incident response by prioritizing high-risk threats and providing security teams with actionable insights to mitigate attacks faster. It reduces response time and improves operational efficiency.
Example: When Stealthwatch detects an unusual login attempt from an untrusted location, it automatically alerts security teams and suggests immediate remediation actions, preventing a possible account takeover.
5. Cloud and On-Premises Network Visibility
Stealthwatch provides comprehensive monitoring across cloud, hybrid, and on-premises environments, ensuring full security coverage and compliance.
Example: A global enterprise uses Stealthwatch to track network traffic across multiple office locations and cloud services, ensuring security across all assets and reducing unauthorized access incidents.
6. AI-Powered Compliance Monitoring and Regulatory Enforcement
AI automates compliance tracking by monitoring network behavior, enforcing security policies, and ensuring adherence to industry regulations.
Example: A government agency uses Stealthwatch to verify compliance with cybersecurity regulations, reducing audit risks and improving data security. The automated monitoring ensures no security breaches go unnoticed.
7. Zero-Day Threat Detection Using AI
AI-powered security monitoring helps identify unknown or zero-day threats by analyzing patterns and detecting anomalies before they become attacks.
Example: A retail company uses Stealthwatch to detect a previously unknown malware variant attempting to spread across internal systems. This allows security teams to contain and neutralize the threat quickly.
Read about Vectra AI.
Pros and Cons of Cisco Secure Network Analytics (Stealthwatch)
Pros
✅ AI-Driven Threat Detection – Uses advanced machine learning for anomaly detection and network security.
✅ Deep Network Visibility – Monitors encrypted traffic, cloud services, and hybrid environments.
✅ Automated Incident Response – Reduces security response time with AI-driven insights and automatic threat containment.
✅ Compliance and Risk Monitoring – Helps organizations meet regulatory security standards and track policy adherence.
✅ Seamless Integration – Works with Cisco’s security ecosystem and third-party solutions.
✅ Real-Time Data Analysis – Continuously learns and adapts to emerging security threats.
Cons
❌ Complex Setup for Large Networks – Requires extensive configuration for enterprise-wide deployment.
❌ High Resource Utilization – AI-powered security analytics can be resource-intensive.
❌ Works Best with Cisco Ecosystem – Optimal performance is achieved when integrated with other Cisco security products.
❌ Pricing Can Be High for Small Businesses – Enterprise-grade pricing may not be feasible for smaller companies.
Conclusion
Cisco Secure Network Analytics (Stealthwatch) is a leading AI-driven network security platform that provides real-time threat detection, automated incident response, deep network visibility, and compliance tracking.
By leveraging machine learning, encrypted traffic analytics, and threat intelligence, Stealthwatch helps organizations monitor security risks, detect anomalies, and respond effectively to cyber threats. Whether for large enterprises, financial institutions, healthcare providers, or government agencies, Cisco Secure Network Analytics delivers a scalable, AI-powered security solution that strengthens cybersecurity resilience in an evolving digital landscape.
Cisco Secure Network Analytics is vital in modern cybersecurity defense strategies because it uses AI-driven automation, behavioral analytics, and adaptive security monitoring.
FAQ: Cisco Secure Network Analytics and AI in Network Security
What is Cisco Secure Network Analytics used for?
It is used to detect, analyze, and mitigate real-time cybersecurity threats.
How does Cisco Secure Network Analytics detect threats?
It uses AI-driven behavioral analytics to identify anomalies and suspicious activities.
Can Cisco Secure Network Analytics monitor cloud environments?
It provides network visibility across on-premises, hybrid, and cloud environments.
How does AI improve Cisco Secure Network Analytics?
AI automates threat detection, risk prioritization, and real-time security alerts.
Does Cisco Secure Network Analytics analyze encrypted traffic?
It uses Encrypted Traffic Analytics (ETA) to detect malware in encrypted data.
How does Cisco Secure Network Analytics handle compliance?
It ensures regulatory compliance by tracking network activity and enforcing policies.
Can Cisco Secure Network Analytics prevent insider threats?
It monitors user and entity behavior (UEBA) to detect abnormal actions.
Does Cisco Secure Network Analytics integrate with other Cisco tools?
It integrates with Cisco Talos, Umbrella, SecureX, and other security solutions.
How does Cisco Secure Network Analytics respond to threats?
It provides automated alerts and incident response recommendations to security teams.
Can Cisco Secure Network Analytics detect ransomware attacks?
Yes, AI analyzes network traffic patterns to detect early signs of ransomware activity.
How does Cisco Secure Network Analytics protect financial institutions?
It detects fraudulent activity and secures financial transactions from cyber threats.
Does Cisco Secure Network Analytics work with third-party security tools?
It can integrate with SIEMs, firewalls, and other cybersecurity platforms.
Can Cisco Secure Network Analytics detect zero-day threats?
Yes, AI identifies unusual behaviors and unknown threats before they become attacks.
How does Cisco Secure Network Analytics handle large enterprise networks?
It scales to monitor thousands of endpoints and detects threats across complex environments.
What industries use Cisco Secure Network Analytics?
It is widely used in finance, healthcare, government, retail, and cloud-based businesses.
How does Cisco Secure Network Analytics reduce security response time?
AI prioritizes critical threats and automates mitigation strategies for quick responses.
Does Cisco Secure Network Analytics work with hybrid cloud networks?
Yes, it provides real-time monitoring and security analytics for hybrid cloud setups.
Can Cisco Secure Network Analytics detect insider threats?
Yes, it identifies unusual user behavior and unauthorized data access attempts.
How does Cisco Secure Network Analytics improve cybersecurity resilience?
It continuously learns from network activity to predict and prevent future threats.
Is Cisco Secure Network Analytics suitable for small businesses?
It is designed for enterprises, but scalable solutions exist for smaller organizations.
