The CISO Playbook for AI Agents | Datadog

August 11, 2026

AI Summary

5 min read

As Datadog CISO Emilio Escobar started rolling out AI coding agents to engineers, he watched a sales rep ask a business intelligence tool a simple question: “How is the enterprise tier team doing?” The agent obliged, pulling the data from a SQL database. The data was technically accessible to anyone who knew SQL well enough—but before AI, no sales rep had ever bothered to learn the queries. The moment crystallized something Escobar already suspected: AI flattens organizations. Permissioning that worked for a world where data access required technical skill no longer works when any employee can prompt their way to it.

Escobar’s approach to AI adoption at Datadog has been the opposite of blocking. Two years ago, he bought ChatGPT licenses for everyone in the company. When fellow CISOs at RSA asked how he could possibly not block these tools, his answer was that blocking doesn’t actually stop people from using them—it just drives usage underground. “The inverse of ‘have to block it’ turned out to be correct, which is the people leaning into it the earliest and the most are the ones that you actually want to reward,” he says. Datadog now has over 4,000 engineers using coding agents, with 98% of all employees using some form of AI.

The credential problem and the sandbox solution

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What you'll learn

1 (00:05) **The Core Problem: What Can AI Agents Do?** - Emilio Escobar frames the central security challenge of AI agents: their access to tools, binaries, and credentials.
2 (02:23) **The Datadog Approach: Mandatory Adoption, Not Blocking** - Emilio explains why Datadog chose to lean into AI tools early, starting with 50 Cursor licenses for engineers.
3 (04:21) **The Data Flattening Problem: AI Exposes Permission Gaps** - AI agents, especially on the corporate side, reveal that existing data permissions are often inadequate.
4 (06:06) **Credential Injection: A Practical Solution for Agent Security** - On the engineering side, a key control is preventing agents from directly accessing static credential files.
5 (07:23) **The "Judge": An AI-Powered Malicious Intent Scanner** - Emilio's team built an internal AI judge that evaluates code for malicious intent, not just CVEs or vulnerabilities.
6 (09:58) **The "Tree is Fixed" Problem: Misaligned Agent Rewards** - Agents are trained on existing code and have reward structures that can lead to unintended, harmful actions.
7 (11:38) **The Helplessness Problem in the Industry** - Emilio observes a sense of helplessness among many security leaders who are waiting for a commercial solution to solve AI agent security.

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Show Notes

a16z's Joel De La Garza is joined by Emilio Escobar, Chief Information Security Officer at Datadog, to discuss what it takes to secure a company where nearly every employee is using AI and more than 4,000 engineers are working with coding agents. Rather than trying to block new tools, Emilio explains why Datadog chose to embrace AI early and build the security infrastructure needed to use it safely.

They unpack how AI changes traditional assumptions around data permissions, credentials, developer access, and software supply chains. Emilio shares how Datadog uses role-based MCP servers and ephemeral credentials, as well as an AI "judge" built by his security team to evaluate the intent behind code and agent skills before they enter the environment.

They also discuss why security teams can't afford to wait for commercial solutions to every new AI threat, how the relationship between developers and security teams needs to change, and why Emilio is less concerned about an AI "escaping" than he is about the sheer volume of vulnerabilities AI could uncover.

Resources:

Follow Emilio Escobar on LinkedIn: linkedin.com/in/emilioesc

Follow Joel De La Garza on LinkedIn: https://www.linkedin.com/in/3448827723723234/

Follow Datadog on X: https://x.com/datadoghq

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