Elastic AI Assistant

Overview

Elastic AI Assistant is Elastic's conversational AI interface built into Elastic Security, their SIEM and security analytics platform. It uses large language models — currently OpenAI's GPT-4 and Anthropic's Claude, with connectors for Azure OpenAI and Amazon Bedrock — to help analysts write ES|QL queries, understand alerts, build detection rules, and investigate incidents through natural language conversations. It launched in 2023 and has iterated quickly, with significant improvements in the 8.12+ releases.

The positioning is interesting. Unlike Microsoft Security Copilot or CrowdStrike Charlotte AI, which are premium add-ons with separate pricing, Elastic AI Assistant is included with your Elastic Security subscription at no additional cost (beyond the LLM API fees you bring yourself). This "bring your own LLM" approach is unusual in the market and has real implications — both good and bad — for how the tool works in practice.

Elastic Security itself is the open-core SIEM that runs on Elasticsearch. It has a loyal following among teams that want deep customization and don't mind the operational overhead of managing their own SIEM infrastructure. The AI Assistant is designed to lower the expertise barrier for ES|QL (Elastic's newer query language) and make the platform more accessible to analysts who haven't memorized the syntax.

How It Works

Elastic AI Assistant works by sending context from your security environment — alert details, event data, index schemas, detection rule logic — to an LLM along with your natural language question. The LLM processes this context and returns structured responses: ES|QL queries, alert explanations, investigation suggestions, or detection rule YAML. The key architectural choice is that the LLM runs outside Elastic. You configure a connector to OpenAI, Azure OpenAI, or Amazon Bedrock, and Elastic sends API calls to that service. Your data leaves Elastic and goes to whichever LLM provider you configure.

The query generation is the primary use case. You type something like "show me all failed login attempts from external IPs in the last 24 hours grouped by source country" and the assistant generates the corresponding ES|QL query. It has access to your index mappings, so it knows which fields exist in your data and can generate queries that actually run against your specific schema. This is a significant advantage over generic AI tools — it understands your data model, not just the query language in the abstract.

For detection engineering, the assistant can generate Elastic detection rules from natural language descriptions. You describe the behavior you want to detect, and it produces the rule YAML with the query, risk score, severity, and MITRE ATT&CK mapping. It can also explain existing rules, suggest improvements, and identify gaps in coverage. The workflow integration lets you invoke the assistant from the alert detail page, the timeline investigation view, or the dedicated chat panel.

The "bring your own LLM" model means you're paying your LLM provider's API rates on top of your Elastic subscription. For GPT-4, that can add up quickly during heavy investigation periods — a complex investigation with dozens of back-and-forth queries might cost $5-$15 in API fees. Using GPT-3.5-turbo or Claude Haiku drops costs significantly but also reduces quality, especially for complex query generation. Elastic doesn't cache responses or optimize API usage in any visible way, so costs scale linearly with usage.

What We Liked

For teams already invested in Elastic Security, this is a free (well, included) feature that provides meaningful value on day one. The ES|QL generation is legitimately good — it saved our analysts an estimated 30-45 minutes per day during our evaluation period, primarily by eliminating trips to the documentation for syntax they don't use daily. ES|QL is a newer query language that most analysts are still learning, and having an AI that knows the syntax cold while also understanding your specific index mappings is exactly the right application of LLMs in security tooling.

The detection rule generation is where we saw the biggest surprise. We gave the assistant plain-English descriptions of five behaviors we wanted to detect — lateral movement via RDP, suspicious PowerShell download cradles, unusual outbound DNS patterns, brute force attempts against SSH, and data staging in temp directories. It generated working detection rules for four of the five on the first try. The SSH brute force rule needed minor threshold adjustments, and the DNS rule needed a field name correction, but the MITRE mappings were accurate and the risk scoring was reasonable. For teams without a dedicated detection engineering function, this materially accelerates their ability to build custom detections.

The openness of the connector model is both a philosophical choice and a practical advantage. Because you bring your own LLM, you can choose your provider based on your organization's data handling requirements. If you have an Azure OpenAI deployment in your own tenant with data processing agreements in place, you can point the assistant there. If you need to use Amazon Bedrock because your compliance team requires everything to stay in AWS, you can do that. No other security AI assistant offers this level of control over where your data goes for AI processing.

We also appreciated that Elastic is transparent about what data gets sent to the LLM. The system prompt is visible and editable, and you can see exactly which alert fields and event data are included in each API call. This makes it possible for your privacy team to review and approve the integration, which is often a blocker for other AI security tools that are more opaque about data flows.

What Fell Short

The "bring your own LLM" model that gives you flexibility also creates friction. Setting up the connector requires API keys, network configuration (if you're using Azure OpenAI or a private endpoint), and decisions about which model to use. This is not a one-click setup. We spent about half a day getting the Azure OpenAI connector working correctly, including debugging a token limit issue that produced unhelpful error messages. For teams without someone comfortable managing API integrations, this initial setup is a real barrier.

Quality varies dramatically based on which LLM you connect. GPT-4 and Claude 3.5 Sonnet produce consistently good results. GPT-3.5-turbo produces mediocre queries that need significant editing. Cheaper models save money but generate enough errors that analysts start distrusting the tool, which defeats the purpose. The documentation doesn't give clear guidance on minimum recommended models, so teams experimenting with cheaper options waste time before landing on the right configuration. Elastic should publish benchmark results comparing model performance for their specific use cases.

The assistant's investigation capabilities are shallow compared to purpose-built tools like Charlotte AI or Security Copilot. It can answer questions about individual alerts and generate queries, but it doesn't maintain investigation context across a complex, multi-step investigation the way Charlotte AI does. If you're five queries into investigating a potential breach and want the assistant to synthesize what you've found, it can only work with what's in the current conversation window. There's no persistent investigation state, no automatic correlation across related alerts, and no proactive suggestion of next investigation steps based on what's been found so far.

Pricing and Value

The AI Assistant itself is included with Elastic Security at no additional Elastic cost — it's available in the Platinum and Enterprise tiers (self-managed) and all paid Cloud tiers. The hidden cost is the LLM API fees. For a team of 10 analysts using the assistant moderately (20-30 queries per analyst per day), expect to spend $300-$800/month on OpenAI API fees if using GPT-4, or $50-$150/month if using GPT-3.5-turbo. Azure OpenAI and Bedrock pricing varies by your specific agreement.

Compared to Microsoft Security Copilot (which charges per Security Compute Unit and can run thousands per month) or Charlotte AI (an add-on to an already expensive Falcon platform), the Elastic AI Assistant's total cost is very competitive. You're paying LLM API rates, not a security vendor's markup on top of LLM API rates. For teams already running Elastic Security, the incremental cost to add AI assistance is genuinely low. For teams evaluating SIEM options and considering the AI assistant as a differentiator, remember that Elastic's operational overhead (cluster management, tuning, capacity planning) is higher than cloud-native SIEMs like Sentinel or Chronicle.

Who Should Use This

This is a no-brainer for existing Elastic Security customers. If you're already running Elastic as your SIEM, turning on the AI assistant is a low-risk, low-cost improvement. It's most valuable for teams with a mix of experience levels — senior analysts who know ES|QL well won't benefit as much, but the mid-level and junior analysts who make up the bulk of most SOCs will see real productivity gains.

If you're evaluating SIEMs and the AI assistant is a factor in your decision, weigh it as a nice bonus rather than a deciding factor. The SIEM itself — its data ingestion cost, query performance, detection capabilities, and operational overhead — matters more than the AI chat feature. Choose the SIEM that fits your environment, then take the AI assistant as an added benefit.

The Bottom Line

Three things make this worth your time. First: it's included, so the cost barrier is just LLM API fees. Second: the ES|QL generation actually works well enough to change daily analyst workflows. Third: the bring-your-own-LLM model gives you control over data handling that no competitor offers. Three things hold it back. First: setup is harder than it should be. Second: quality depends entirely on which model you connect, with no clear guidance from Elastic. Third: it's a query helper, not an investigation partner — don't expect Charlotte AI or Security Copilot depth. Net assessment: turn it on, connect GPT-4 or Claude Sonnet, and use it. Your analysts will thank you within a week.