Deep Instinct Prevention Platform
Deep learning endpoint protection with >99% accuracy and <0.1% false positives.
Vendor Information
Deep Instinct Prevention Platform Overview
Deep Instinct is a preemptive data security company that applies deep learning—the most advanced form of artificial intelligence—to cybersecurity. The company developed DSX (Data Security X), the world's first and only purpose-built deep learning cybersecurity framework, which prevents zero-day threats and unknown malware with >99% accuracy and a <0.1% false positive rate in <20 milliseconds—750x faster than the fastest ransomware can encrypt. Deep Instinct is the pioneer in applying end-to-end deep learning to cybersecurity, fundamentally shifting the industry from reactive "assume breach" detection to proactive, prevention-first security.
Founded in 2015 in Tel Aviv, Israel by Guy Caspi, Dr. Eli David, and Nadav Maman, Deep Instinct is headquartered in New York City with R&D operations in Tel Aviv. The company has raised $322M in funding from investors including PayPal Ventures, BlackRock, NVIDIA, and Millennium Technology Value Partners. In September 2022, Lane Bess (former CEO of Palo Alto Networks and COO of Zscaler) became CEO, bringing over 35 years of cybersecurity experience to lead the company's growth.
Deep Instinct DSX Platform consists of two core components: DSX Brain—a proprietary discriminative neural network trained on tens of billions of data points over a decade—and DIANNA (Deep Instinct's Artificial Neural Network Assistant)—a GenAI-powered threat explainability engine. DSX Brain provides pre-execution prevention by analyzing files before they execute, stopping known and unknown threats including zero-day attacks, ransomware, and APT attacks. DIANNA complements prevention with expert-level malware analysis in <10 seconds. The platform protects endpoints (Windows, macOS, Linux, Chrome OS), servers, mobile devices (iOS, Android), cloud storage (Amazon S3, Amazon FSx NetApp), NAS environments (Dell CAVA, NetApp Vscan), and applications through agentless scanning, eliminating the need for cloud lookups and requiring only 1-2 updates per year compared to daily updates required by traditional solutions.
Key Capabilities
Standardized capabilities mapped to this product's security niche
Executes endpoint response actions automatically upon confirmed detection (process termination, file quarantine, registry key removal, and ransomware rollback), without waiting for analyst approval. Scope of automated actions and rollback fidelity are the primary quality differentiators.
Blocks exploit techniques at the point of execution (memory injection, process hollowing, credential dumping), independent of whether the exploited application or CVE is known.
Detects and blocks malware using behavioral analysis and ML models rather than signature matching. Prevents execution of known and novel malware including script-based and fileless attacks.
Detects ransomware encryption activity using behavioral signals and restores affected files from shadow copies or local snapshots. Rollback depth and speed are primary quality metrics.
Scans container image layers for OS package CVEs and application dependency vulnerabilities at build time, registry push, or pre-deployment, before execution.
Captures a continuous record of workload events (process, network, file, syscall), for forensic investigation: differentiation is event retention period, query performance, and contextual enrichment.
Detects in-memory exploitation techniques (shellcode injection, heap spraying, ROP chains), in running workloads without relying on file-based signatures.
Monitors process execution, network connections, and file system activity in running workloads using a kernel agent, eBPF sensor, or sidecar container to detect behavioral anomalies and known attack patterns.
Monitors AWS Lambda, Google Cloud Functions, and Azure Functions for behavioral anomalies, insecure configurations, and runtime threats during execution.
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