Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Indirect Prompt Injection Attacks: A Lurking Risk to AI Systems

The rapid adoption of AI has introduced a new, semantic attack vector that many organizations are ill-prepared to defend against: prompt injection. While many security teams understand the threat of direct prompt injection attacks against AI agents developed by their organizations, another more subtle threat lurks in the shadows: indirect prompt injection attacks.

Why AI Security Requires Context: Introducing Issues & the Correlation Agent

Data is never the problem. Security teams rarely complain about having too much of it. The real danger comes from data that sits unconnected and unexplained. What teams actually need is data that is actionable and converges into meaning. Data that cuts deeper than surface level signals. Data that reveals what is unfolding and what needs to happen next.

Safe Harbor: An Open Source "Abort Mission" Button for Your AI Agent

AI agents are increasingly connecting to more systems and workflows. They read structured data, follow multi-step instructions, and can reach deep into applications and developer environments. The same capabilities that make them powerful also create new opportunities for attackers. As Zenity Labs continued to study these emerging attack classes, we noticed a pattern starting to appear.

Your Browser is Becoming an Agent. Zenity Keeps It From Becoming a Threat.

Agentic browsers are quickly becoming part of everyday work. Tools like ATLAS, Comet, and Dia can read web content, navigate SaaS tools, interpret instructions, and act on behalf of a user. They promise faster execution and higher productivity but they also introduce new risks that traditional security tools are not designed to see. As these browser-based agents spread across both managed and unmanaged devices, the enterprise attack surface grows in ways that most teams can’t quantify.

The AI Answering Service Wake Up Call for Modern Businesses

Here is the 4 a.m. question almost no owner says out loud. What actually happens when a real customer calls your business and you do not pick up? You assume it is not that bad and maybe they leave a voicemail. You think maybe they call back or your AI answering service catches it next time. The truth from the inside is uglier and I should know because I helped build the systems that make those calls disappear. I have watched good businesses bleed out through their phone lines while everyone stares at the wrong metrics.

Inside the Agent Stack: Securing Agents in Amazon Bedrock AgentCore

In the first installment of our Inside the Agent Stack series, we examined the design and security posture of agents built with Azure Foundry. Continuing the series, we now focus on Amazon Bedrock AgentCore, a managed service for building, deploying, and orchestrating AI agents on AWS.

Securing the New AI Edge: Why Salt Security Is Bringing MCP Protection to AWS WAF

The definition of the "edge" is changing. For years, security teams have focused on the traditional perimeter: web applications, public APIs, and user interfaces. We built firewalls, deployed WAFs, and established strict access controls to keep bad actors out. But with the rapid adoption of Agentic AI, the perimeter has expanded. Today, your "edge" isn't just where users connect to your apps; it's where AI agents connect to your data.

Is AI taking entry-level jobs a good thing? #cybersecurity #ai #podcast

There's growing concern that AI automation is removing the hands-on experience junior analysts need to develop into senior defenders. In this Intel Chat, Matt Bromiley and Chris Luft challenge that assumption. Matt breaks down why the traditional entry-level path of endless log review and alert triage was never the best training ground to begin with. Log detection, alert triage, and drift detection are often cited as how defenders learn the trade. But most analysts never had time to get to drift detection because they were buried in repetitive work.

Stop Gambling on Compliance: Why Near100% Recall Is the Only Standard for AI Data

LLMs, agents and retrieval‑augmented models are increasingly being adopted for product analytics, customer support and decision‑making workflows. With that scale comes exposure: AI privacy and security incidents incidents involving customer PII are more common than ever and becoming a compliance issue. Let’s look at the statistics: These underscore the importance of robust guardrails and why relying on privacy tools with mediocre recall is a gamble.