Skip to main content

Observability

Aiceberg’s Role

Aiceberg gives leaders clear visibility into how consumers and employees use LLMs like ChatGPT and Microsoft Copilot—capturing both intent (tasks being pursued) and entity (topics engaged) signals. It also pinpoints code generation activity, including code present, code request, and code vulnerability signals, so organizations can guide AI use with precision.

  • safe, compliant scaling of AI across the enterprise
  • stronger policy enforcement
  • targeted training on proper use of AI
Aiceberg Guardian Agent dashboard screenshot showing safety signals, security signals, request metrics, and donut charts for internal actions and response actions with a line graph of token volume.
Intent classification bar listing categories such as accounts support, education learning, finance legal, health wellness, parenting, and workplace assistance, used by Aiceberg’s AI firewall to understand user intent and enforce LLM security.

Understand What Drives Every AI Interaction

Every enterprise wants to understand why users interact with their AI systems. Aiceberg makes that possible. With over 250 intent classifications, our Guardian Agent reveals what users are truly trying to accomplish, offering visibility into the motivations behind every prompt or command. It’s not just monitoring — it’s understanding behavior at scale.

But insight means little without trust. Aiceberg bridges the gap between human intent and AI action, ensuring every interaction aligns with policy, compliance, and ethical use. You’ll know exactly what your AI is doing — and why — so you can scale confidently without fear of misuse or drift.

Semantic network visualization of concepts and keywords connected across multiple clusters, illustrating how Aiceberg’s observability tools map relationships between user intents, risk topics, and compliance triggers in AI firewall systems.

Know Who and What Your AI Is Talking About

Visibility into what your AI systems are engaging with is as important as knowing why. Aiceberg gives enterprises a complete picture of the entities—people, data types, and systems—interacting with their AI models. This helps identify trending topics, sensitive subjects, or emerging areas of risk across your organization’s AI activity.

Armed with these insights, security teams can spot patterns that signal potential vulnerabilities or data exposure. Aiceberg ensures that your model’s training data and conversations stay aligned with approved business priorities and compliance requirements—so you can harness AI innovation without losing control of the narrative.

Prompt content dashboard verifying that requested and present code match across programming languages including C++, CSS, Python, JavaScript, Ruby, and more, with all items passing security checks in Aiceberg's AI firewall.

Monitor and Manage AI-Generated Code in Real Time

AI can generate powerful code—but without oversight, it can also introduce serious risk. Aiceberg continuously monitors for the presence of code within AI interactions, giving enterprises control over when and where AI-generated code is permitted. With language-specific visibility, you can easily identify when AI is being used to produce or manipulate source code across your environment.

Our granular, language-aware controls let you define and enforce what’s acceptable, keeping your development pipelines compliant and secure. Whether you’re managing open-source contributions or protecting proprietary IP, Aiceberg ensures every line of AI-generated code aligns with your organization’s security and usage policies.

Prompt content panel showing AI firewall detection of requested code languages including C++, C#, Java, HTML, SQL, Python, CSS, PHP, Ruby, with flagged detection of JavaScript in red; part of Aiceberg's agentic firewall.

Control What Gets Asked for—Before It’s Written

Aiceberg flags prompts and instructions requesting code the moment they appear. You get clear visibility into who’s asking for what, across languages and repositories, so risky requests never slip through unnoticed.

Set language-based rules or block code requests entirely. With policy-driven controls, you can allow what’s safe, stop what’s not, and keep AI-assisted development aligned with your security standards and compliance requirements.

Catch Weaknesses Before They Ship

Aiceberg extends Code Present with real-time reviews of any code surfaced in AI interactions. We automatically flag insecure patterns, hard-coded secrets, and risky dependencies—language-aware and inline—so issues are caught before they enter your pipeline.

Use policy-driven, language-specific rules to block high-risk code, allow with warnings, or auto-route for review. Teams get clear remediation guidance and trend visibility, reducing MTTR and keeping AI-assisted development compliant, resilient, and ready for scale.

Get Started with Aiceberg

Book My Demo

Send AI interactions to Aiceberg

Route employee AI prompts and responses from LLMs like ChatGPT into Aiceberg in real-time.

Aiceberg analyzes

It detects intent, entities, patterns, and anomalies across language, code, and behavior to surface hidden risks.

Forward filtered signals to your SIEM

Only actionable, policy-relevant events are sent to your security tools.

Review Aiceberg dashboard

Access metrics, trends, and insights to guide training, enforce policies, and optimize AI adoption.