AI Runtime Inspection & Enforcement · The Prevention Edge of AIDR

See it. Understand it. Stop it — before it executes.

Observability across both AI data planes.Read the intent inside every interaction.Neutralize before it executes.

No Kernel Agent

Host sensor · no kernel shim

Model-independent

No GenAI, LLM, SLM or MCP gateway — in the path or beneath it

Zero token cost

No per-token metering — flat, predictable TCO

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$4.99M

global average cost of a data breach — up 12% year over year, a record high

IBM, Cost of a Data Breach Report 2026

56%

increase in AI-driven attacks, led by deepfake impersonation and AI-enabled malware

IBM, Cost of a Data Breach Report 2026

$1.93M

saved by organizations making extensive use of AI and automation in security

IBM, Cost of a Data Breach Report 2026

The Problem

Your teams are using AI tools you can’t see

Prompts, tool calls, coding copilots, and autonomous agents — the AI your workforce adopted moves through a plane your security stack was never built to watch, let alone stop. The exposure is already inside your environment. You just can’t see it yet.

Your employees are using AI tools you can’t see. SalienceCyber.ai fixes that. It captures every AI interaction on the endpoint and turns it into one clean audit trail — visibility and enforcement in the same product, without an LLM sitting in the critical path or another EDR integration to maintain.

Robert FormerCareer Cybersecurity Expert & Seasoned CISO

The Evidence

The exposure is measured, not asserted

Adoption outran governance. Attackers noticed. These are the figures from the 2026 breach data: what is ungoverned, what is being exploited, and how long anyone takes to notice.

43%

of breached organizations had a shadow AI incident, up from 20% a year earlier

[1]

68%

of breached organizations had no governance to manage or detect unauthorized AI use, against 63% in 2025

[1]

1 in 4

malicious breaches were AI-enabled, up 56% year over year, at an average of ~$6M across that entire class

[2]

All eight figures, with sources →

Sources

[1] IBM Security, Cost of a Data Breach Report 2026, July 2026. Source for every figure marked [1]. ibm.com/reports/data-breach
[2] IBM Newsroom, “One in Four Malicious Breaches are AI-Enabled, Costing Companies $6 Million on Average,” 29 July 2026. Source for the ~$6M average across the whole class of AI-enabled malicious breaches. newsroom.ibm.com

In the console

This is what seeing it looks like

Console enclave graph with Claude, ChatGPT, Gemini, Grok, Copilot and Perplexity as linked nodes among devices, people and external destinations. — Three of eight views, cycling. The same telemetry, read at the altitude each role works at.Take the full product tour →

Network Sensor table of process-attributed egress flows with counters for total flows, attributed flows, AI flows and evaded flows. — Three of eight views, cycling. The same telemetry, read at the altitude each role works at.Take the full product tour →

The Platform

One platform, two data planes

SalienceCyber.ai watches AI activity in the browser and on the host, and can stop it in either. By combining an AI-aware browser extension with a lightweight System Sensor, organizations can See — Comprehend — Neutralize across every AI tool, GenAI session, and agentic workflow, and across both the host and browser data planes.

Two sensors run on one endpoint and feed one decision loop, the CognitionAI Engine. The Browser Sensor runs in the browser session, in-page, reading prompts, GenAI sessions and tool calls across Chrome, Chromium, Edge and Safari, and acts at the point of interaction. The System Sensor runs in userspace on native OS APIs, reading processes, agents and AI-created artifacts across Windows, macOS and Linux, and acts at the point of execution. Neither adds kernel surface: no driver, no kernel extension, no EDR hook, no Ring 0 component, and neither requires root or administrator privilege.
	One decision loopCognitionAI Engine
	Browser planeBrowser Sensor	System planeSystem Sensor
Where it runs	In the browser session, in-page	In userspace, on native OS APIs
What it reads	Prompts · GenAI sessions · tool calls	Processes · agents · AI-created artifacts
Kernel surface	None — the extension runs in-page, not on the host	None — no driver, kernel extension, EDR hook or Ring 0
Platforms	Chrome · Chromium · Edge · Safari	Windows · macOS · Linux
Where it acts	At the point of interaction	At the point of execution

Two planes. One decision loop. No kernel agent on either.

The platform correlates AI activity across three key telemetry planes, under a single user, process, and session identity:

01

AI Traffic Visibility

Intercepts and decodes AI-related network traffic to reveal model interactions, prompts, responses, and data movement.

02

Process Attribution

Associates every AI connection with its originating process, and identifies applications attempting to bypass approved AI channels.

03

Filesystem Provenance

Tracks what AI accesses and what it creates — maintaining lineage even when AI-generated files or binaries are executed later, outside the original session.

Together, the host sensor and browser extension provide end-to-end visibility from user interaction to endpoint execution — one audit trail, with a named user and process behind every AI action, human or agentic, and no kernel-based monitoring to carry.

Explore the Platform

The alert is the incumbent's business model.

See the CognitionAI Engine stop an AI-forged attack before it runs.

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