What is shadow AI?
Shadow AI is the use of generative AI tools by employees without the knowledge, approval or oversight of an organization's IT or security teams.
Security | Threat Detection | Cyberattacks | DevSecOps | Compliance
The Latest Cybersecurity News & Insights From Around The World
Browser AI Events in the SOC: What to Send and What to Suppress
Browser-layer AI monitoring produces events, and the natural next step is forwarding them to the security operations center. Consider what they arrive into. Industry research for 2026 puts false positives at close to half of all alerts, with around forty-two percent going entirely uninvestigated. Browser AI events are behavioral anomaly alerts, and behavioral anomaly alerts are the category analysts already deprioritize, precisely because they are noisy by nature.
AI Governance in Financial Services: The Use Case Sets the Rules
An AI governance framework tells a financial institution to inventory its systems, assess risk and document decisions. Consumer protection law tells it something different and considerably harder, which is that a model unable to produce specific reasons for a credit denial cannot lawfully be used to make one. That distinction is what separates AI governance in financial services from AI governance generally.
A revisit of remote Spectre attacks on Cloudflare Workers
In 2021, we assessed remote Spectre attacks against Cloudflare Workers. Based on the results, we shipped a production defense called Dynamic Process Isolation (DyPrIs), which identifies maliciously looking scripts and isolates them into separate processes. Since then, newer techniques in the area of stabilizing Spectre attacks have been discovered. To understand if these techniques posed a threat to our Workers production environment, we decided to internally reassess the remote Spectre attack.
Threat Detection for AI for Enhanced Security Monitoring
Securonix has released a Value Package with OOTB policies for detecting threats across AI platform, identities, agents, data movement, endpoints, and network activity.
ChatGPT Security Risks for Enterprises: Real Incidents, Controls and Best Practices
Security teams often evaluate ChatGPT by examining its outputs. The greater risk, however, lies in the information employees submit before the model generates a single response. As generative AI becomes a big part of daily business operations, prompts increasingly contain confidential customer data, proprietary source code, legal documents, and strategic plans.
Benchmaxxing: When the Benchmark Becomes the Target
Public benchmarks in AI provide important signals and allow for regression testing, directional validation of model updates, and public discussion of capabilities and limitations. But the more attention a benchmark receives, the stronger the incentive to optimize for it. Once a score becomes the goal, teams start benchmaxxing: optimizing for the benchmark rather than the capability it is meant to measure. This is a familiar problem in the AI space.
7 API Security Requirements for Payment Transactions
Every payment flow your organization runs, from card authorization to ACH transfers to embedded lending to open banking consent, is now an API call. That’s good for velocity. It’s also why payment APIs sit at the top of the attack surface for financial services and enterprise SaaS platforms handling money movement.
How to Choose a React Native Development Company for AI-Driven Mobile Projects
AI-driven mobile apps grow more complex every month. The gap between a team that can actually ship one and a team that merely claims they can is wider than most product managers expect. Wrong hires cost you four to six months of rework on top of the initial build, a brutal, avoidable tax. So if you're planning a mobile product that uses machine learning, on-device inference, or real-time AI features, vendor selection deserves far more rigor than skimming a portfolio and firing off a request for proposal.
10 Best Kubernetes Security Tools in 2026 [Open-Source]
Somewhere right now, someone is spinning up a fresh Kubernetes cluster, feeling pretty good with a basic firewall, skimmed a hardening guide, and maybe even applied a few CIS benchmarks. What could possibly go wrong? Roughly 18 minutes. That’s the average time before a newly exposed Kubernetes cluster receives its first malicious probe or attack attempt. So there’s zero room for error when it comes to securing Kubernetes.