CrowdStrike, Inc.

Methods and systems that identify unusual or suspicious patterns in data streams, often leveraging machine learning models trained on normal behavior, to detect threats, faults, or significant events as they occur.

Applying artificial intelligence and machine learning techniques to enhance cryptographic systems, such as generating encryption models, improving zero-trust architectures, or enabling privacy-preserving computations like federated learning.

Systems and methods for identifying and blocking unauthorized access, malicious activities, or abnormal behavior within a network by analyzing traffic, system logs, or behavioral patterns.

Tools and processes for assessing, monitoring, and improving the security configuration and external accessibility of resources deployed within cloud computing environments.

Mechanisms to facilitate the secure exchange of data between different entities or systems while enforcing usage policies, managing digital content rights, and ensuring data consistency during replication or transfer.

Systems and methods for collecting, processing, and ensuring the quality and consistency of data used for network monitoring, asset management, and operational decision-making, including conflict detection and reliability scoring.

Systems and methods utilizing artificial intelligence, particularly large language models and neural networks, to extract, summarize, generate, or categorize information from unstructured or semi-structured data sources.

Techniques for generating human-like text or other content using large pre-trained models, often involving prompt engineering, speculative decoding, or multi-modal inputs for content creation.

Systems and methods for authenticating users, devices, or applications, authorizing their access to resources based on policies, and managing digital identities across various platforms.

Methods and systems for integrating, transforming, and managing complex or domain-specific data from disparate sources into a unified structure, often for specific applications like social networks, genomics, or business forms.

Methods and systems for identifying, extracting, and structuring specific entities, relationships, or insights from text-based documents, often involving techniques like named entity recognition, relation extraction, or summarization.

Systems and methods for automating the lifecycle of machine learning models, including pipeline deployment, model management, versioning, and configuring for different inference environments.

Utilizing machine learning, particularly deep learning, to analyze medical data such as images, sensor readings, or physiological signals for disease prediction, diagnosis, or treatment assessment.

Technologies enabling the creation and management of virtual computing environments, including virtual machines and virtual desktops, with an emphasis on secure and efficient remote access, updates, and performance.

Systems and methods for automatically deploying, configuring, and updating network devices and services, including software updates, client onboarding, and topology management across various network types.

Methods and systems for efficiently allocating computing resources, balancing workloads, and managing power states to improve performance, reduce energy consumption, or enhance reliability in computing platforms.

Techniques for combining and analyzing information from multiple distinct data modalities (e.g., text, image, video, audio, sensor data) to derive richer insights or improve system performance and decision-making.

Techniques employed within compilers or related tools to analyze program code, identify entities for compilation, and optimize execution on target hardware, including reconfigurable systems, to improve performance or resource efficiency.

Technologies for deploying, managing, and governing applications and services in cloud environments, particularly focusing on containerization, microservice architectures, API gateways, and distributed data management.

Techniques for protecting data at rest or in backup, ensuring its integrity, confidentiality, and verifiable origin, often involving encryption, unique identifiers, or secure repositories.

Algorithms and hardware optimizations for rapidly identifying and characterizing relevant visual features (e.g., objects, motion, gradients) from images or video streams, often integrating machine learning for feature representation and recognition, with a focus on real-time performance and reduced computational cost.

Methods for training machine learning models across multiple decentralized devices or servers while keeping data localized, often involving aggregation of model parameters and secure communication.