Cyber-threat detection

Help information security teams reduce risk and improve their security posture efficiently and effectively

Automate threat detection across large numbers of devices, attack vectors, and data silos

Continuous assessment of asset inventory to gain a complete and accurate view of devices, users and applications with access to IT systems

Models that can detect and respond to deviations from the norm, even with noisy data

Prediction models that will assess where and how a company is most likely to be breached, so planning and resource allocation can be directed toward weak points in the IT system 

Explainability of model recommendations and analysis for security operations leaders, CISOS, auditors and Board of Directors

Get up to date knowledge of global and industry level threats to help prioritize defense systems

Prevent cyber threat incidents and respond quicker/better when they do happen, improving OPEX

Free up limited cybersecurity teams to focus on complicated cases, while AI takes care of routine tasks

Classification Models that can identify threatening vs non threatening events and actors

Anomaly detection, entry classification, domain generation detection

Unsupervised learning for unlabeled data, clustering data based on anomalies

Analyze large data sets of events to identify many different types of threats (eg, malware, ransomware, email phishing, malicious code downloads)

Train neural networks to tell the difference between malicious and safe files

Use images to train classifier neural networks to detect malware in .doc and .pdf files

Bias reduction models