Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

What we learned about AI agent security by monitoring our agents

AI agents comprise models, instructions, data, and tools, so thoroughly investigating potential security risks requires evidence from several components. As Datadog teams build AI agents for internal workflows, we use Datadog AI Guard to monitor how they handle each component during a session. We’ve found that application logs may capture an agent’s final API call without showing which prompt, retrieved content, or tool result led to the action.

When AI adoption outpaces IT visibility

At 1Password, we started expanding our use of AI with a familiar IT playbook. We identified the problems we wanted to solve and the tools that could help us achieve those goals. The plan was straightforward: enable teams, move quickly, learn what worked, and build the visibility needed to manage the cost.

Top 7 Technology Strategies Growing Businesses Need to Stay Competitive

Growing businesses face mounting pressure to modernize their operations while competitors race ahead with emerging technologies. The gap between those who adapt and those who fall behind widens each quarter, making strategic technology adoption no longer optional but essential for survival. Seven core strategies have emerged as critical differentiators in today's market, each addressing specific operational challenges that determine whether a company scales successfully or stagnates. Understanding these approaches reveals why some organizations thrive while others struggle to keep pace.
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Why AI is becoming harder to budget for

For most business technology, the cost is relatively easy to understand. You buy a licence, agree a contract or pay for a certain level of usage, and you have a reasonable idea of what you will spend over the year. AI is making that much harder. As businesses move beyond individual AI subscriptions and start using AI across more of their operations, costs can vary considerably depending on which models are being used, how often they're being used and what they're being asked to do.
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Identity Everywhere: Bringing Infrastructure Identity to Agentic IT

Every era of computing eventually exposes the assumptions that made the last era work. For decades, networking succeeded because it was mostly identity agnostic. Packets moved because they had addresses. Routers and switches forwarded traffic because the network knew where something was going, not necessarily who or what was behind it. That model helped build the internet and modern enterprises. But it is not enough for the era we are entering now.

Agent Incident Response: Containment Is the Easy Part

Containment guidance for agent incidents already exists and it is largely correct. Revoke the tokens, freeze the orchestration tier, cut egress, set the vector store to read-only. Those steps take minutes and any competent team will find them. ‍ The difficulty sits either side of containment. Deciding what kind of incident this is takes longer than stopping it, establishing what the agent did before you stopped it takes longer still, and both depend on preparation that has to exist beforehand.

AI/LLM Penetration Testing in 2026: The Complete Guide

Most organisations now run at least one LLM in production, and a growing number run agents that call tools and act without a human in the loop. The security testing those systems receive was designed for deterministic software. AI applications fail differently. The payload is natural language, the same input can be safe nine times and unsafe on the tenth, and the malicious instruction often arrives inside a document or tool description rather than from the user.

Why Your AI Application Is Exposed

Imagine getting three separate security reports back for your new enterprise AI assistant: On paper, the application looks ready for production, but in reality, a threat actor bypasses your guardrails in minutes. How? By using the AI model as an intermediary. The attacker steers the LLM to invoke the internal utility tool, thereby bridging an untrusted prompt directly to the backend execution sink.

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.