Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Threat actors continue to rely on social engineering as AI is incorporated into their attacks, according to ESET’s Threat Report for the first half of 2026. ESET’s Director of Threat Prevention Labs, Jiří Kropáč, stated, “Rather than relying on entirely new methods and tools, attackers are quickly adapting established techniques to new platforms, technologies, and user behaviors.

Coding agent security has a framing problem. Most security conversations around these tools center on the wrong question. 'Did the agent have permission to do that?' is a reasonable place to start, but in the context of autonomous AI systems, it's not where the risk actually lives. In Zenity Labs' research into the coding agent threat model, the pattern that keeps surfacing isn't that agents are doing things they aren't allowed to do.

I remember the days when merely saying “AI” was enough to earn glares for bringing up such a taboo subject, almost equivalent to saying “Voldemort.” Now, AI is at the center of many people’s daily lives and certainly at the center of business operations. I find myself using AI for everyday tasks. Unfortunately for security teams, the bad guys are using it too.

Accelerating security solutions for small businesses‍ Tagore offers strategic services to small businesses. A partnership that can scale‍ Tagore prioritized finding a managed compliance partner with an established product, dedicated support team, and rapid release rate. Standing out from competitors‍ Tagore's partnership with Vanta enhances its strategic focus and deepens client value, creating differentiation in a competitive market.

Agentic AI and generative AI both build on large language models, but they behave in fundamentally different ways once deployed. Generative AI produces content in response to a specific prompt and then stops. Agentic AI receives a goal, then autonomously plans, decides, and executes multi-step workflows to accomplish that goal, often across systems and tools the enterprise runs. That difference is the difference between an AI that helps a human do work faster and an AI that does the work itself. ‍

In a controlled enterprise lab, we tested how far an agentic attack stack could go by harnessing a frontier model with an agent platform, MCP-enabled tooling, operational context, and enough autonomy to execute a complete attack path.