Proactively secure your environment from API security vulnerabilities, misconfigurations, and design flaws. Protect APIs from attacks in real-time with automated detection and response powered by Noname Security Machine Learning AI innovations accelerated by Intel. Deliver secure APIs faster with pre-production testing.
Offerings
Offering
Noname Security is the only company taking a complete, proactive approach to API Security. Noname covers the entire API security scope across four pillars: Discovery, Posture Management, Runtime Security, and API Security Testing. Noname Security collects relevant data from every type of API including HTTP, RESTful, GraphQL, SOAP, XML-RPC, and JSON-RPC and easily deploys with automatic updates across cloud-hosted, self-hosted, hybrid, and distributed deployments. The Noname API Security Platform inspects API traffic, headers, query parameters, message request and response body JSON or XML messages for SOAP, REST, and GraphQL to deliver unprecedented visibility and security of your API estate. With Noname Security, customers can: - Accelerate an effective API security program - Protect APIs and critical assets from Cyber Attack - Deliver secure apps and APIs faster with Shift Left - Maintain compliance with regulatory requirements, data residency rules, and internal policies while reducing overhead with remote engines to aggregate traffic into a centralized console, reducing traffic and maintaining tight data control. This includes compliance frameworks such as CWE, NIST, PCI-DSS, and more.
Offering
Leverage the Intel® Trust Authority to attest that the most recent innovations from Intel® are properly configured on GCP instances with Intel® SGX processors for container-level Anjuna seaglass enclaves. Remote Attestation adds the ability to verify that the application is indeed running inside a Trusted Execution Environment (TEE) and is running the expected trustworthy version of the software. Thereby preventing a malicious insider or bot from running the application outside a TEE, or inside a TEE but with a malicious version of the code. With this ability, enterprise DevSecOps and CISO teams can verify that the AI model or API gateway application server is indeed trustworthy. Intel® TDX processors (in preview) for VM-level enclaves are also available to encrypt the memory of process space of the API gateway or server hosted on GCP; and the Noname Security remote machine learning engines to create a Trustworthy AI solution architecture. As organizations look to minimize their cyber risk, confidential computing has come to the forefront as a revolutionary approach to data security. Confidential computing ensures that sensitive information remains encrypted and protected, even while in use by applications, processes, or services, using powerful hardware security features in modern CPUs for isolation of sensitive code and data in operation. Unlike traditional security methods that focus on securing data at rest or in transit, confidential computing extends protection to data while it's being processed, ensuring its confidentiality throughout its full lifecycle. When combined with the power of artificial intelligence (AI) and machine learning (ML), confidential computing enhances API security by protecting sensitive data, ensuring secure execution environments, verifying trust, isolating workloads, and facilitating compliance with regulatory requirements. By leveraging confidential computing and AI, organizations can deploy their own private machine learning instances that are purpose-built for securing API traffic rather than utilizing a public cloud API service, drastically reducing their attack surface. What are the benefits? Despite best-in-class security access controls, rogue system administrators or workloads running on untrusted infrastructure can increase the risk of sensitive data exposure. Confidential computing enhances protection from both internal and external threats by helping organizations maintain control over data, mitigate the risk of data breaches, and achieve compliance with stringent data protection regulations. Confidential computing addresses several key security concerns, including: Data Confidentiality: Organizations that handle high volumes of sensitive data such as healthcare, finance, and government, need confidential computing to ensure that their valuable data remains encrypted and inaccessible to unauthorized entities, even when processed or analyzed in untrusted environments. Secure Processing: By leveraging hardware-based security mechanisms such as secure enclaves, confidential computing enables organizations to perform computations on encrypted data without exposing it to the underlying infrastructure. This mitigates the risk of data breaches and insider threats, enhancing the overall security posture. Regulatory Compliance: Confidential computing solutions help organizations comply with stringent data protection regulations such as GDPR, HIPAA, and PCI DSS by safeguarding sensitive data throughout its lifecycle. This reduces the potential for regulatory fines and penalties associated with data breaches or non-compliance. Scalability and Performance: Despite stringent security measures, confidential computing solutions offer scalability and high-performance computing capabilities, allowing organizations to process large volumes of sensitive data efficiently without compromising on speed or reliability.
Offering
Leverage the Intel® Trust Authority to attest that the most recent innovations from Intel® are properly configured on Azure instances with Intel® SGX processors for container level Anjuna seaglass enclaves. Remote Attestation adds the ability to verify that the application is indeed running inside a Trusted Execution Environment (TEE) and is running the expected trustworthy version of the software. Thereby preventing a malicious insider or bot from running the application outside of a TEE, or inside a TEE but with a malicious version of the code. With this ability enterprise DevSecOps and CISO teams can verify that the AI model or API gateway application server is indeed trustworthy. Intel® TDX processors (in preview) for VM level enclaves are also available to encrypt the memory of process space of the API gateway or server hosted on Azure and the Noname Security remote machine learning engines to create a Trustworthy AI solution architecture. As organizations look to minimize their cyber risk, confidential computing has come to the forefront as a revolutionary approach to data security. Confidential computing ensures that sensitive information remains encrypted and protected, even while in use by applications, processes, or services, using powerful hardware security features in modern CPUs for isolation of sensitive code and data in operation. Unlike traditional security methods that focus on securing data at rest or in transit, confidential computing extends protection to data while it's being processed, ensuring its confidentiality throughout its full lifecycle. When combined with the power of artificial intelligence (AI) and machine learning (ML), confidential computing enhances API security by protecting sensitive data, ensuring secure execution environments, verifying trust, isolating workloads, and facilitating compliance with regulatory requirements. By leveraging confidential computing and AI, organizations can deploy their own private machine learning instances that are purpose-built for securing API traffic rather than utilizing a public cloud API service, drastically reducing their attack surface. What are the benefits? Despite best-in-class security access controls, rogue system administrators or workloads running on untrusted infrastructure can increase the risk of sensitive data exposure. Confidential computing enhances protection from both internal and external threats by helping organizations maintain control over data, mitigate the risk of data breaches, and achieve compliance with stringent data protection regulations. Confidential computing addresses several key security concerns, including: Data Confidentiality: Organizations that handle high volumes of sensitive data such as healthcare, finance, and government, need confidential computing to ensure that their valuable data remains encrypted and inaccessible to unauthorized entities, even when processed or analyzed in untrusted environments. Secure Processing: By leveraging hardware-based security mechanisms such as secure enclaves, confidential computing enables organizations to perform computations on encrypted data without exposing it to the underlying infrastructure. This mitigates the risk of data breaches and insider threats, enhancing the overall security posture. Regulatory Compliance: Confidential computing solutions help organizations comply with stringent data protection regulations such as GDPR, HIPAA, and PCI DSS by safeguarding sensitive data throughout its lifecycle. This reduces the potential for regulatory fines and penalties associated with data breaches or non-compliance. Scalability and Performance: Despite stringent security measures, confidential computing solutions offer scalability and high-performance computing capabilities, allowing organizations to process large volumes of sensitive data efficiently without compromising on speed or reliability.
Offering
Noname Security creates the most powerful, complete, and easy-to-use API security platform that helps enterprises discover, analyze, remediate, and test all legacy and modern APIs. Discover - Find and inventory all APIs. Analyze - Detects attacks, suspicious or anomalous behavior, and misconfigurations using AI-based behavioral analysis powered by Noname's custom machine learning algorithms accelerated by Intel innovations. Remediate - Prevent attacks by leveraging noname as a machine learning policy decision point that integrates with existing WAFs and API Gateways security infrastructure. Test - Noname Security's Active Testing product helps to shift left to verify the integrity of APIs before deployment.
Offering
Unleash Telco-Grade API Security from Edge to Cloud — Introducing the Intel® NetSec Accelerator with Intel® Ethernet Controller E810 and Noname Security Remote Engine Bundle! Harness the power of the Intel® NetSec Accelerator Reference Design and 4th Gen Intel® Xeon® Processors with Noname Security Remote Engines to achieve low-latency, high-bandwidth, telco-grade API security. With the Noname Security machine learning remote engine analyzing network traffic moving directly through the Intel network card, enterprise organizations experience faster discovery of API risks without having to send a copy of the network traffic to a log or a cloud-hosted machine learning system. Whether in the core of the service mesh or on the edge of the cloud, this solution offers unparalleled agility, performance, and feature sets with 5G network connectivity to meet complex and dynamic security requirements. With support for all major cloud providers including AWS, Azure, and GCP, this powerful solution is tailored for hybrid-cloud use cases, such as secure PCI credit card API GETs, healthcare FHIR HL7 JSON API Posts, and secure transmission of JPEG images. Thanks to Noname Security’s mature integrations, enterprise organizations gain additional value from their existing investments, and consolidate the complexity of multiple heterogeneous tools such as API gateways, WAFs, and load balancers.
Offering
Anjuna seaglass container enclaves include Remote Attestation for the ability to verify that the API gateway (e.g. Kong) is indeed running inside an AWS Nitro based Trusted Execution Environment (TEE) and is running the expected trustworthy version of the software. Thereby preventing a malicious insider or bot from running the application outside a TEE, or inside a TEE but with a malicious version of the code. With this ability, enterprise DevSecOps and CISO teams can verify that the AI model or API gateway application server is indeed trustworthy. As organizations look to minimize their cyber risk, confidential computing has come to the forefront as a revolutionary approach to data security. Confidential computing ensures that sensitive information remains encrypted and protected, even while in use by applications, processes, or services, using powerful hardware security features in modern CPUs for isolation of sensitive code and data in operation. Unlike traditional security methods that focus on securing data at rest or in transit, confidential computing extends protection to data while it's being processed, ensuring its confidentiality throughout its full lifecycle. When combined with the power of Noname Security artificial intelligence (AI) and machine learning (ML), confidential computing enhances API security by protecting the sensitive data being sent from the Noname Kong plugin to the Noname remote machine learning engine. By also running the Noname machine learning engine inside confidential computing enclaves the result is Trustworthy AI facilitating compliance with regulatory requirements. By leveraging confidential computing and AI, organizations can deploy their own private machine learning instances that are purpose-built for securing API traffic rather than utilizing a public cloud API service, drastically reducing their attack surface. Despite best-in-class security access controls, rogue system administrators or workloads running on untrusted infrastructure can increase the risk of sensitive data exposure. Confidential computing enhances protection from both internal and external threats by helping organizations maintain control over data, mitigate the risk of data breaches, and achieve compliance with stringent data protection regulations. Confidential computing addresses several key security concerns, including: Data Confidentiality: Organizations that handle high volumes of sensitive data such as healthcare, finance, and government, need confidential computing to ensure that their valuable data remains encrypted and inaccessible to unauthorized entities, even when processed or analyzed in untrusted environments. Secure Processing: By leveraging hardware-based security mechanisms such as secure enclaves, confidential computing enables organizations to perform computations on encrypted data without exposing it to the underlying infrastructure. This mitigates the risk of data breaches and insider threats, enhancing the overall security posture. Regulatory Compliance: Confidential computing solutions help organizations comply with stringent data protection regulations such as GDPR, HIPAA, and PCI DSS by safeguarding sensitive data throughout its lifecycle. This reduces the potential for regulatory fines and penalties associated with data breaches or non-compliance. Scalability and Performance: Despite stringent security measures, confidential computing solutions offer scalability and high-performance computing capabilities, allowing organizations to process large volumes of sensitive data efficiently without compromising on speed or reliability.