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Artificial intelligence (AI) is helping security teams modernize how they detect, investigate, and respond to threats — not by replacing analysts or reinventing cybersecurity, but by making existing workflows faster, smarter, and more efficient. For enterprises with rich internal data and well-established security practices, AI is a natural next step.

With the right foundation, organizations can quickly adopt AI to streamline detection, consolidate tooling, and speed up investigation and response. And unlike the hype around “AI-powered attacks,” the real value lies in using AI to extract insights from your own environment — so decisions are grounded in context that’s specific to your business.

“The real key to effective AI in cybersecurity is giving it access to the data that makes your environment unique, and typically, this is data which is traditionally hard to operationalize in a cyber security context,” says James Spiteri, director of product management for generative AI and machine learning at Elastic. Elastic Security runs on the Elastic Search AI Platform, enabling fast, contextual analysis across vast volumes of enterprise data.

What makes AI work in the enterprise

Getting value from AI in security doesn’t require a complete overhaul. It’s about building on what you already have — data, processes, and people — with smart tools that enhance productivity and reduce complexity.

Here are a few things to look for in a security analytics platform designed to scale with your team:

Designed for security analysts

Modern AI-powered platforms help analysts move faster — not start over. Natural language interfaces let them ask questions in plain English, generate queries automatically, and find answers without learning a new language or user interface.

Tailored to your environment

Prebuilt detections are a great starting point, but real precision comes from connecting your own data. Whether it’s endpoint activity, cloud telemetry, or business logic, the more the platform knows about your environment, the more useful its insights become.

Elastic supports this through a rich set of connectors that bring structured and unstructured data — files, records, logs — into Elasticsearch. Once indexed, AI models can generate context-aware alerts, enrich investigations, and power automation with precision.

Flexible and transparent by design

Security teams need to understand how AI makes decisions. Platforms like Elastic emphasize transparency, with features that allow teams to inspect model behavior, track usage, and audit interactions. Flexibility also matters, so you can choose the right model (or models) for your use case, without being locked in.

Making the most of AI: What leading teams are doing

Across Elastic’s customer base, the most successful AI implementations share a few common practices:

1. Integrate organizational data early

Customers that feed their internal data into the platform from day one unlock faster value. By syncing key sources to Elasticsearch, they give AI the context it needs to prioritize what matters.

2. Choose the right language model for the job

With Elastic’s model-agnostic approach, organizations can use the large language models that best meet their latency, cost, or accuracy requirements — or even run multiple models to support different functions.

3. Embrace genAI for everyday tasks

Whether it’s writing queries, troubleshooting detections, or customizing rules, generative AI assistants save time. Security analysts can ask virtually anything about day to day and get clear, in-context answers, reducing the ramp-up time for new tools.

4. Automate the right workflows

AI doesn’t replace analysts — it frees them from repetitive, manual work. Detection, enrichment, and initial triage are increasingly being automated with confidence. With the right integrations, teams can extend automation into incident response and remediation.

The bottom line

Deploying AI for cybersecurity doesn’t have to be complicated. With platforms like Elastic Security, organizations can build on their existing data, tools, and team knowledge — and see value quickly. Whether you’re aiming to scale operations, reduce response times, or enable less experienced analysts to be more effective, AI-powered analytics help you do more with what you already have.

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