Upwind has added a feature to its cloud application detection and response (CADR) platform, allowing real-time detection of threats to application programming interfaces (APIs). The platform uses machine learning algorithms to collect telemetry data from Layers 3, 4, and 7 of the networking stack, enabling the identification of deviations and anomalous behavior in API traffic. The goal is to reduce the time required to investigate API security incidents by up to 10 times and mean time to response times by up to seven times. In the age of generative artificial intelligence (AI), there is a growing focus on API security. Many organizations are discovering that sensitive data is being shared inadvertently with AI models. Historically, responsibility for securing APIs has been unclear, with many cybersecurity teams assuming that application development teams are securing them as they are developed. However, this can lead to thousands of APIs that cybercriminals can exploit to exfiltrate data or modify business logic. Over the next 12-18 months, organizations plan to increase software security spend on APIs, DevOps toolchains, incident response, open source software, software bill of materials, and software composition analysis tools. Advancements in AI and eBPF technologies could simplify the entire software development lifecycle by streamlining the collection and analysis of telemetry data.
Microsoft Sentinel enables more accurate event reconstruction by integrating Endace’s one-click, drill-down access to definitive, full packet evidence and SIEM workflows
Endace has partnered with Microsoft Sentinel to integrate EndaceProbe with the cloud security solution. This integration allows NetOps and SecOps teams to access full packet evidence from Microsoft Sentinel, enabling faster investigations and more accurate event reconstruction. This integration also enhances security teams’ ability to respond to threats with confidence. Benefits of the integration include: Streamlined investigation workflows, alerts, and playbooks from Microsoft Sentinel, with one-click, drill-down access to definitive, full packet evidence captured by EndaceProbe; Continuously capture weeks or months of full packet data, across Hybrid, On-Prem, and Multi-Cloud environments; Single central console for searching and analyzing recorded packet data across global scale networks, integrated with Microsoft Sentinel; Deep visibility that shows exactly what happened before, during, and after every event; Zero-Day Threat (ZDT) risk validation using playback of recorded network traffic; Combining EndaceProbe’s centralized search with Microsoft Sentinel’s AI-powered SIEM enables faster, more efficient incident investigation and resolution; Military-grade Security: EndaceProbe appliances are FIPS 140-3 compliant and are listed on the DoDIIN APL.
BigID’s privacy management solution helps enterprises to capture, score, and track AI-related privacy risks in a centralized register to strengthen governance and enable effective risk mitigation
BigID, announced the launch of AI Privacy Risk Posture Management – the industry’s first solution to help organizations manage data privacy risks across the AI lifecycle. With unmatched visibility, automated assessments, and actionable privacy controls, BigID empowers enterprises to govern AI responsibly while staying ahead of fast-evolving regulations. BigID’s platform help organizations: 1) Automatically Discover AI Assets: Quickly inventory all models, vector databases, and AI pipelines across hybrid environments to understand how sensitive and personal data flows through AI systems – a critical requirement for GDPR Article 35 and beyond. 2) Proactively Manage AI Data Lifecycles: Enforce policies for data minimization, retention, and lawful purpose across training and inference, preventing model drift and limiting risk exposure. 3) Streamline Privacy Risk Management: Capture, score, and track AI-related privacy risks in a centralized Privacy Risk Register to strengthen governance and enable effective risk mitigation. 4) Accelerate AI Privacy Impact Assessments: Use pre-built, customizable templates for DPIAs and AIAs aligned to regulatory frameworks – with automated evidence capture to simplify documentation. 5) Automate Risk Visibility & Reporting: Gain up-to-date reporting and dynamic risk assessments to demonstrate compliance and communicate AI risk posture to regulators and stakeholders. 6) Board Ready Privacy Metrics: Deliver meaningful KPIs and metrics to DPOs and board leaders, helping quantify AI privacy risk and monitor remediation efforts.
Cyera’s AI DLP solution automatically detects unique data in real-time and prevents exfiltration, controls data used in AI tools and prompts to prevent data exposure
Cyera, the world’s fastest-growing data security company, today announced the launch of Omni DLP, a breakthrough AI-native solution that finally delivers on the promise of enterprise data loss prevention. Omni DLP combines the power of Cyera’s AI-native Data Security Posture Management (DSPM) with a real-time DLP analysis engine from its Trail Security acquisition, creating a unified architecture that protects data at rest, in motion, and in use. With Omni DLP, organizations gain: 1) AI-Powered Noise Reduction – Eliminate over 95% of false positive alerts to focus on the few most critical and actionable 2) Real-Time, Adaptive Protection – Automatically detect your unique data and prevent exfiltration 3) Deep AI Governance – Control data used in AI tools and prompts, and prevent data exposure through AI systems. 4) 360 View – all your endpoint, network, email, messaging and cloud DLP risks, alerts and policies in a single view, leveraging AI for enrichment and correlation. 5) Policies That Learn – auto-tuned controls that evolve with your data. “Omni DLP is the brain DLP has been missing,” said Yotam Segev, CEO and co-founder of Cyera. “Omni DLP enables us to protect sensitive data in motion – the crown jewels – automatically classified by our AI-native classification engine. This is data security the way it was meant to be: intelligent, adaptive, and built for the AI era.”
HiddenLayer’s AISec platform 2.0 enhances explainability of AI models using Model Genealogy and AI Bill of Materials (AIBOM), that reveal their lineage and pedigree to track how they were trained, fine-tuned, and modified over time
HiddenLayer released AISec Platform 2.0, the platform with the most context, intelligence, and data for securing AI systems across the entire development and deployment lifecycle. Tnew release includes Model Genealogy and AI Bill of Materials (AIBOM), expanding the platform’s observability and policy-driven threat management capabilities. With AISec Platform 2.0, HiddenLayer is establishing a new benchmark in AI security where rich context, actionable telemetry, and automation converge to enable continuous protection of AI assets from development to production. With AISec Platform 2.0, HiddenLayer empowers security teams to Accelerate model development, Gain full visibility, Automate model governance and enforcement and Deploy AI with confidence. AISec Platform 2.0 introduces: 1) Model Genealogy: Unveils the lineage and pedigree of AI models to track how they were trained, fine-tuned, and modified over time, enhancing explainability, compliance, and threat identification. 2) AI Bill of Materials (AIBOM): Automatically generated for every scanned model, AIBOM provides an auditable inventory of model components, datasets, and dependencies. Exported in an industry-standard format, it enables organizations to trace supply chain risk, enforce licensing policies, and meet regulatory compliance requirements. 3) Enhanced Threat Intelligence & Community Insights: Aggregates data from public sources like Hugging Face, enriched with expert analysis and community insights, to deliver actionable intelligence on emerging machine learning security risks. 4) Red Teaming & Telemetry Dashboards: Updated dashboards enable deeper runtime analysis and incident response across model environments, offering better visibility into prompt injection attempts, misuse patterns, and agentic behaviors.
Pentera’s distributed orchestration platform lets security teams run simultaneous security validation tests via single interface through a choice of persistent or dynamic attack nodes deployed across multi-site infrastructures at scale
Automated Security Validation platform Pentera is setting a new standard for enterprise-scale security validation with the introduction of its Distributed Attack Orchestration architecture and AI-reporting capabilities. These enhancements meet the requirements of security teams to scale security validation testing to govern a consistent security posture across decentralized enterprise IT architectures. With a choice of persistent or dynamic attack nodes deployed across multi-site infrastructures, security teams can run simultaneous security validation tests coordinated through a single interface. Each node runs in-depth attack emulation, ensuring that as testing scales across the enterprise, the depth and rigor of validation remain uniform. Designed for centralized control, Pentera provides security teams with the following capabilities to manage distributed testing efficiently: Granular Test Scheduling, Real-Time Control over Test Operations, Silent Runs – Pentera provides advanced control over test noise levels, with signed commands and payloads, allowing operators to test across environments without overloading the SOC with false alarms. “Our Distributed Attack Orchestration solution provides visibility into how adversaries can exploit the enterprise attack surface, while our AI-based reporting aggregates the trends security leaders need to prioritize to reduce exposure across the organization,” said Ran Tamir, Chief Product Officer at Pentera.
Snyk’s AI-powered security testing solution assists developers to proactively detect, inventory and deal with risks that can occur when using generative AI and APIs in software development
Cybersecurity company Snyk announced the launch of Snyk API & Web, a new dynamic application security testing or DAST solution designed to meet the growing demands of modern and increasingly AI-powered software development. The new service integrates technology from Probley, a startup acquired by Snyk into Snyk’s application security platform. The technology unifies critical AppSec testing techniques into a single developer security platform. The DAST service seeks to assist in dealing with risks that can occur when businesses increasingly leverage generative AI and use APIs to bridge the gap between LLMs and the applications they fuel. Snyk argues that APIs introduce vulnerabilities that can expose AI models to significant risks, jeopardizing the security of entire software supply chains. Snyk API & Web offers a robust solution for developers and AppSec teams to proactively discover, inventory and secure API vulnerabilities before they become threats. The new service offers tools designed to simplify DAST for developers and security teams. The inetgration also leverages AI-driven capabilities to detect vulnerabilities that are often missed by conventional methods. This makes the solution especially useful in fast-paced development environments where speed and accuracy are paramount. API & Web also includes an AI-powered API Security Testing engine that uses generative AI and traditional machine learning models. The engine helps developers better map the growing API attack surface and automate the process of scanning for vulnerabilities.
Entro Security’s gen AI adds context to exposed secrets and non-human identity risks by creating structured, natural language summaries and auto-classifying each finding based on metadata
Entro Security, unveiled a set of GenAI capabilities that bring more context, clarity and control to exposed secrets and NHI-related risks across enterprise environments. The new engine, powered by large language models (LLM), enriches Entro’s security findings with structured, natural language summaries. Each finding is automatically classified based on metadata and context, making it easy for security teams to understand what each NHI does, where exposed secrets live and what’s at risk. This release builds on Entro’s previously launched GenAI ownership attribution model, which automatically assigns a human owner to each exposed secret or NHI using a smart multi-source hierarchy. Together, these capabilities drive faster triage, smarter remediation and clearer accountability across the NHI lifecycle. Entro’s platform now leverages explainability to provide generated summaries for secrets findings – classifying the target service , implementation type, potential purpose and more. Security teams no longer need to chase down vague pattern matches across environments or guess what the unknown secret is doing. The GenAI engine also automatically reduces noise, enables smarter and faster remediation, built for scale and compliance.
Automated code review platform Coana allows security teams to determine whether identified vulnerabilities in a codebase are actually exploitable by constructing detailed call graphs through static control-flow analysis
Supply chain security startup Socket has acquired cloud-based automated code review software startup Coana ApS for an undisclosed sum. Coana’s offerings include reachability analysis, a method that determines whether identified vulnerabilities in code dependencies are actually exploitable within a specific application. The approach involves constructing detailed call graphs through static control-flow analysis to identify which parts of the code are reachable and which are not, allowing developers to focus on genuine threats. The startups says its methodology significantly reduces false positives by over 80% compared with traditional software composition analysis tools by filtering out irrelevant alerts to allow security teams to prioritize and remediate critical vulnerabilities more efficiently. The technology can be easily integrated into existing development workflows and works on-premise without the need for complex configurations, according to the company. Coana will bring powerful static control flow and call graph analysis to Socket’s platform, allowing teams to prioritize vulnerabilities based on whether they’re actually exploitable in a given codebase.
Startup Sentient’s new system for deploying AI applications in Trusted Execution Environments uses confidential computing to ensure full data isolation, verifiability and attestation
Peter Thiel-backed AI development startup Sentient is looking to differentiate itself in terms of security with the launch of a new system for deploying AI applications in Trusted Execution Environments. The new Sentient Enclaves Framework v0.70 brings the concept of “confidential computing” to AI development. It’s meant to ensure full data isolation, verifiability and attestation for AI applications, the company said. It uses Amazon Web Services Inc.’s AWS Nitro Enclaves technology to ensure that neither AWA nor the host system is able to access or modify AI workloads. In that way, it says, it provides rock-solid guarantees around AI data security. The Sentient Enclaves use AWS Nitro as a foundation to ensure that applications run as intended, without any possibility of nefarious actors making unauthorized modifications. They’re fully open source too, meaning they’re accessible to anyone who’s interested in using them. With Sentient’s platform, developers can work together on the development of open-source large language models that rely on shared datasets and decentralized computing resources. Its platform is built on blockchain technology, and its ecosystem uses cryptocurrency to reward participants based on their contributions.