Common Use Cases
Consumer facing companies must be able to gather and manage the right data, develop intuitive features, and apply analytics that generate the insights effective to their action and business plan. Along with providing cloud services to future products, one of the biggest challenges is the operational and log collection of connected devices.
Consumer electronics companies, like no other, have a unique opportunity to learn from their customers and their use habits. At SFL Scientific, we leverage data science tools to create custom platforms and systems that monitor, learn, and inform our users. With many new tech areas emerging into the market, we're here to minimize the risk with innovation and get back to what sales and a good customer experience means: People eager for exciting technology.
Traditional security solutions like SIEM, intrusion, and endpoint security tools, built on known and identified rule sets and signatures, cannot scale to fully meet the demand of advanced cybersecurity operating environments and incoming threats.
We used advanced tools, machine learning, behavior, and predictive modeling to monitor, identify, and secure businesses from the latest attacks.
Emerging Vectors: Leverage data science to build tools and models to identify and predict compromised credential, zero-day, and low & slow attacks. Build active threat maps and predict the next attack.
Scalable Solutions: We build tools that grow as data needs increase, leveraging commodity hardware to keep costs low as data volumes increase.
Lateral Movement: Identify anomalous user-level access. Predict next events and generate immediate security alerts and focused monitoring.
User Behavior Models: Build historical behavioral profiles for each user based on complex features monitored in real-time. Deploy graph models and recommendation systems comparing historical information to real-time behavior.
Contextual Data: Using pen-source tools, combine powerful frameworks and libraries to leverage historical information, time-series data, and contextual data to detect sophisticated attacks.
As attacks increase in complexity and volume - especially in growing IoT and multi-entry environments - it is difficult for traditional analytical tools and infrastructure to secure information due to data volume, response times, and scalability.
Full Data: Security is a big data problem. For a security solution that detects all changes, we develop tools that explore RAW information, not filtered streams. Identify anomalies in device, employee, contractor, device, and network activity.
Big Data & AI: Deploy machine learning to build powerful solutions using baseline behavior analytics, identifying past and current performance and user activity. Track accounts, systems, points of entry, and on the cloud, in real-time.
Threat detection must be able to identify changing use patterns and execute complex analysis rapidly, in real-time. Controls and response tools for such threats and fraud detection must be automated to drive business value and sustain managed security services and teams.
We help incident response teams effectively monitor, solve, and address security incidents, providing both audits of existing systems and building solutions to close loopholes, predict next points of entry, and proactively respond to incoming threats.
Data privacy, loss, and fraudulent behavior can have untold damage on company operations and reputation, let's build advanced custom solutions to gain the proper insights for proper action.