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SAS® for Cybersecurity

Fast detection of cyberthreats through big data analytics

Protect your data and network from both insider and outsider threats. Quickly detect potentially malicious activity hidden in massive volumes of data. SAS for Cybersecurity arms you with sophisticated analytics that enable you to take a proactive, targeted approach to identifying and countering cyberthreats – before the damage is done.


Safeguard systems and information.

Identify possible threats and predict future attacks far enough in advance to forestall cyberattacks. We combine network behavior analysis with traditional threat intelligence and other detection techniques to identify more suspicious cyberactivities than any one method alone. By recognizing potential threats sooner, you can take action to prevent data loss and system breaches, and protect your reputation.

Gain true situational awareness.

See the full scope of cyberevents – from entry into the network environment to data access over extended periods of time. With SAS, you can bring together all relevant data and look beyond perimeter traffic for known bad events. Enrich your internal and external network communications, including lateral networks, with contextual information to detect the highest-risk events.

Monitor huge data volumes.

Detect hostile activity anywhere on your network with real-time monitoring of massive amounts of data. Powerful behavioral analytic techniques can reveal anomalies and connections  you would otherwise miss. You can also uncover subtle patterns of behavior that may indicate zero-day and advanced persistent threats (APTs).

Stay abreast of trends and adversaries.

Visual data exploration and a self-learning feedback loop keep your analytic models up-to-date with tactics, techniques and procedures (TTP) that change often to evade detection. The models evolve continuously throughout the analytic life cycle, ensuring that as your adversaries get more sophisticated, so do your detection methods.


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  • Data enrichment. Augments network flow and log file data with business context information and external threat and intelligence data to produce a comprehensive view of cyberrisk.
  • High-performance behavioral analytics. Derives intelligence from all your data by correlating seemingly unrelated data points to predict cyberevents in advance.
  • Multilayered analytics. Performs in-stream recognition of normal and abnormal behaviors to support real-time decision making, and detects previously hidden events using powerful, in-memory predictive analytics.
  • Real-time monitoring. Streams data into a highly-tuned analytic engine, fuses it with organizational and contextual data, and prioritizes events for review.
  • Event management. Streamlines investigations by scoring, prioritizing and routing events – along with contextual information – for analyst review.
  • Data exploration. Enables visual exploration of large amounts of post-event data for further analyzing events and identifying trends that can influence your monitoring strategies. 
  • Machine learning. Keeps models up to date throughout the analytic life cycle through a continuous learning and improvement process.

Recommended Resources

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