Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Secure AI Agents, Everywhere: Why Prompt Injection Is Only Part of the Problem

Ask AI to Choose a prompt Write a TLDR of this post Explain the security risk Summarize what CISOs should know The rules have changed. In every AI deployment, the agent itself is now part of the threat model, and that's a first for enterprise security. Prompt injection gets most of the attention, and for good reason: it doesn't require access to source code, credentials, or network infrastructure. It exploits the fundamental mechanism by which language models process instructions.

Inside SECNAP: An Agentic MDR Platform Built on LimaCharlie

Joshua Strickland from SECNAP shows how his team built a full agentic MDR platform on top of LimaCharlie. SECNAP layers its own customer portal and SOC workflows directly on LimaCharlie's API, giving AI agents the same access to telemetry and response actions as a human analyst. Joshua will walk you through the customer portal, the SOC dashboard, and a live attack simulation on a sandboxed machine, including how AI agents handle tier one and tier two triage with Sonnet and Opus, and how a human analyst reviews and approves response actions before anything ships.

Inside the AI-Accelerated Cyber Underground

Cyberattacks take shape long before a breach through exposed systems, vulnerable software, stolen credentials, underground tools, and attacker experimentation. In this webinar, Emma Stevens, Threat Intelligence Researcher at Bitsight, and Qionglu Lei, Senior Product Marketing Manager at Bitsight, explore what Bitsight research reveals about AI-enabled attacker behavior and the changing cyber underground.

The Problems We Solve Are Not Sexy

The problems we solve are not sexy. That is Garrett Hamilton on Inflection Point: Digital Intelligence Podcast, and he means it as a badge of honor. Problems that have been around for 30 years are rarely glamorous. They are also the ones that take the business down when they go wrong. Why is it more important than ever to tackle these problems?

What is DLL hijacking, and why your new AI plugin might be the easiest way in

DLL hijacking is a decades-old Windows vulnerability class (Mitre Att@ck T1574) getting new life from AI plugins bolted onto legacy desktop apps. Attackers plant a malicious DLL where a vulnerable app will load it instead of the real one, inheriting that app's privileges. To detect it, watch for DLLs loaded by name from writable folders. To prevent it, you have to fix the app's load-order logic or blocking untrusted DLLs at the endpoint.

What Is Data Loss Prevention (DLP)? Types, Use Cases, and Best Practices

Protecting sensitive data is no longer just a compliance objective. It has become a prerequisite for adopting cloud services, enabling AI, and maintaining customer trust. Yet many organizations still struggle to balance innovation with effective data governance. A 2025 Forrester Total Economic Impact study commissioned by Microsoft found that organizations using mature data protection capabilities, including data loss prevention (DLP), achieved a 30% reduction in the likelihood of data breaches.

The Double Edge of AI in Healthcare: Guarding Data, Growing Empathy

Healthcare has always run on two currencies: information and trust. Patients hand over their most sensitive data, diagnoses, genetic profiles, mental health histories, on the assumption that it will be protected and that the people (or systems) handling it will treat them with care. Artificial intelligence is now reshaping both sides of that bargain at once.

What makes a good AI coworker? With OpenAI's Codex product lead

Zero-Shot Learning is a podcast about how AI is built, secured, and deployed. Hosted by Nancy Wang, 1Password CTO, and Dev Tagare, Senior Director of Engineering at Google Gemini, it offers a builder’s view of the architecture and complex decisions involved in shipping AI.

Agentic AI Security: Credentials and Permissions Define the Blast Radius

In July 2026, researchers at Noma Labs coaxed GitHub's new Agentic Workflows into leaking data from a private repository. It wasn’t from malware. They created a plausible-looking issue in a public repository containing instructions for the agent to retrieve information from other repositories in the organization. After testing variations of the prompt, they found that adding one word, "Additionally," was enough to get past GitHub's guardrails.