Release Notes 10/13/25

Release Notes - October 13, 2025

Summary

This release introduces Use Cases for managing complex multi-profile agentic workflows, expanding Aiceberg's capabilities for securing sophisticated AI agent deployments. We've added the Discount Seeking intent signal for e-commerce security, resolved critical blocking issues with Code Requested signals, and enhanced the Monitoring interface with improved session visualization. These updates strengthen Aiceberg's position as the premier platform for monitoring and securing autonomous AI agents in production environments.

API Changes

Use Case Support: The event analysis API now accepts use_case_id parameters, enabling security monitoring for complex agentic workflows that span multiple profiles and interaction types. Use Cases support agent-to-agent, agent-to-LLM, and agent-to-tool interactions within unified security policies.

New Features

Use Cases for Agentic Workflows

Organizations deploying autonomous AI agents can now configure Use Cases that apply multiple security profiles across complex interaction flows:

Multi-Profile Orchestration: Define security policies for agentic systems where different profiles apply to:

Agent-to-LLM communications (instruction generation, knowledge retrieval)
Agent-to-tool interactions (API calls, database queries, external system access)
Agent-to-agent collaboration (task delegation, information sharing)
User-to-agent head messages

Unified Monitoring: Track security signals across all interaction types within a single Use Case, providing complete visibility into agentic workflow behavior and security posture.

This feature addresses the emerging market need for security visibility into autonomous agent systems where traditional single-profile monitoring is insufficient.

Discount Seeking Intent Detection

Added new intent signal specifically designed for e-commerce and customer service applications to detect when users are attempting to manipulate AI agents into providing unauthorized discounts or price reductions. This capability helps organizations:

Protect revenue by identifying discount manipulation attempts
Monitor for social engineering attacks targeting customer service agents
Ensure AI agents follow pricing policies consistently

The signal is fully integrated into Profile configuration and displays in prompt details with probability scores.

SIEM Integration

Added integration point for SIEM providers, enabling organizations to forward Aiceberg security data to their existing data warehouses and analytics platforms for centralized security operations and compliance reporting.

Improvements

Monitoring & Visualization

Session Indentation: Session views now use visual row indentation instead of left-side blue lines, creating a more intuitive conversation thread visualization that makes multi-turn interactions easier to follow.

Radar Chart Completeness: Resolved issue where security signals were missing from the radar chart on the Dashboard, ensuring complete at-a-glance visibility into security posture.

Collection Last Fired: Added "last fired" timestamps to collection displays in Monitoring, making it easier to identify which test suites have been recently executed and need attention.

Signal Detection

Code Requested Blocking: Fixed critical issue where Code Requested signals weren't properly blocking interactions in Enforce mode, closing a security gap for organizations preventing code generation in sensitive contexts.

Sentiment Trace Data: Resolved issue where sentiment analysis was creating traces with empty text, which was cluttering trace views and affecting analysis accuracy.

Intent Data Visibility: Corrected missing intent data in trace views, restoring complete signal detection visibility for security analysis.

Illegality Signal Display: Removed redundant "illegality" pill from trace views that was showing "no refs" alongside specific subcategory indicators (e.g., cyber crimes). This eliminates confusion and maintains consistency with other signal categories that display only their specific subcategory flags.

LLM Security Label: Corrected signal labeling where "LLM Security" was appearing instead of the more specific "Instruction Override" designation. Trace views now consistently display the appropriate instruction override pills at the top level, improving clarity when analyzing adversarial attack attempts.

Bugs Fixed

Resolved HTML rendering errors after profile deletion that were preventing proper page display
Fixed slash/circle icon overuse throughout the UI, improving visual clarity
Corrected checkbox rendering issues in Cannon page that were preventing proper run selection
Fixed API key checkbox rendering problems in API Management
Eliminated duplicate data queries for collection "last fired" dates, improving page load performance
Resolved invalid input handling that was causing unclear error messages
Fixed test environment issues affecting Cannon execution and prompt classification

UI/UX Enhancements

Profile Action Icons: Made each profile action icon visually distinguishable, reducing errors when users need to quickly access specific profile management functions.

Profile Defaults: Changed default profile settings on creation to better align with common enterprise security requirements, reducing initial configuration time.

This release expands Aiceberg's capabilities significantly into the agentic AI space, providing organizations with the security visibility and control they need as they deploy increasingly autonomous AI agents. The combination of Use Cases, enhanced intent detection, and continued performance improvements positions Aiceberg at the forefront of AI security monitoring platforms.