The startup develops a cybersecurity platform that employs artificial intelligence and machine learning for real-time monitoring, signal processing, and threat detection. This technology provides embedded cybersecurity systems and malware detection, enhancing the protection of connected devices against cyber threats.
Funding
$520K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Connected devices are increasingly vulnerable to cyberattacks, leading to potential disruptions, data breaches, and significant financial losses for manufacturers and users. Traditional cybersecurity solutions often rely on signature-based or static analysis, which are insufficient to detect novel and sophisticated "zero-day" malware and attack vectors targeting embedded systems. Protecting IoT devices with limited resources requires innovative approaches that go beyond conventional security measures.
Solution
Parcoor provides embedded cybersecurity solutions that leverage machine learning and system monitoring to protect connected devices from threats and vulnerabilities. The company's technology employs real-time, non-intrusive system monitoring to gain insights into the deep core of embedded systems. This data is then fed into an in-house, cutting-edge machine learning layer designed for resource-constrained devices, enabling high-precision and autonomous threat detection, including zero-day malware. Parcoor's solutions offer both predictive maintenance capabilities by detecting anomalies and hardware failures, and threat detection by identifying attack vectors and malware.
Target Audience
Parcoor's primary customers are manufacturers and users of connected equipment, particularly those in industries where device availability and security are critical, such as healthcare, manufacturing, and high-frequency trading.
Features
- Real-time, non-intrusive system monitoring for in-depth insights into embedded device behavior
- In-house machine learning layer designed for resource-constrained devices (TinyML)
- Anomaly detection for predictive maintenance, identifying potential downtimes and hardware failures
- Threat detection capabilities, including zero-day malware detection and attack vector identification
- Industry-grade improvements based on academic research for enhanced protection
- Packaged and modular solutions for fast and uncomplicated deployment
- API for transmitting relevant data to on-premise or cloud servers
- Interactive dashboard for graphically representing model inputs and outputs