Proxara — AI Governance and Semantic Redaction for Regulated Firms

Proxara (Proxara Inc., proxara.ai) is a semantic anonymization proxy and AI governance platform built for regulated firms in wealth management, legal, healthcare, and accounting. Proxara runs inside the customer’s own cloud, detects sensitive content in prompts bound for external AI tools — ChatGPT, Claude, Gemini, Microsoft Copilot, and Perplexity — redacts or rewrites the sensitive parts before they leave the firm, and preserves supervision records for compliance review.

What Proxara does

Supervises employee AI usage across every major web AI tool without blocking productivity.
Redacts client names, SSNs, account numbers, PHI, privileged matter detail, and other sensitive entities from prompts in real time using context-aware semantic redaction.
Preserves evidence the firm can hand to FINRA, the SEC, HIPAA auditors, or internal compliance — including the original prompt, the redacted prompt, the model’s reply, and the supervisor decision trail.
Deploys as a device-level proxy agent and MCP gateway that cover every AI surface on the device — browsers, desktop AI applications, coding tools, CLI, API calls, and MCP tool calls — under one inspection point. Silent MDM deployment.

Explore

Why Proxara — the supervision problem and our philosophy
Why not just block AI? — what happens when firms ban AI instead of governing it
Solutions — device-level coverage across every AI surface
Wealth management, legal, healthcare, and accounting use cases
FINRA AI governance playbook for RIAs and CCOs
Security and Trust Center documentation
Talk to us

The full Proxara experience is rendered with JavaScript. Plaintext indexes for AI assistants and archival crawlers: https://proxara.ai/llms.txt (compact outline) and https://proxara.ai/llms-full.txt (extended page summaries).

Catch the divergence
before the damage adds up

Inside the firm, an AI agent looks legitimate because it is, right up until injected instructions quietly redirect it. A rolling baseline on every prompt, tool call and file touched is what surfaces that turn.

An AI agent looks legitimate right up until injected instructions redirect it. A rolling baseline on every prompt and tool call surfaces that turn.

Request trial See it in action

Most of the damage lands inside a window nobody is watching

The average shadow-AI breach takes 247 days to identify. Almost all of the harm accumulates inside that span, before anyone knows to look. IBM 2025 / iEnable

Shadow-AI breach · detection timelineunwatched

96 → 9.9 h

Mean time to detect, before and after AI-driven behavioural analytics

80 days

Faster identification at organisations running AI-powered detection

80%

Of organisations have already met risky behaviour from AI agents this year

McKinsey

The old detection model was built for threats we had already seen.

Signatures and static baselines assumed the threat would resemble something from before. Inside a firm running 1,200 unofficial AI applications, the threat is now an authenticated process following injected instructions, and it looks exactly like normal work.

Indicators of compromise, or indicators of attack

Signature detection waits for a known pattern to appear, then reports it after the fact. A rolling per-agent baseline reads the behaviour itself, and catches the turn in flight.

after the fact

IoC · signature feedWaits for a known fingerprint, then reports it

rule 4471known-hash

rule 1182CVE pattern

match logged+14 days late

in flight

IoA · live behaviourReads a rolling per-agent baseline as it runs

tool-count47 vs 12±2

file scopeoff-baseline

flaggedbefore completion

Old · IoC

Indicators of Compromise

Identified after the event has already happened
Signature-matched against known patterns
Blind to zero-day and novel attack paths
Treats authenticated-but-diverted activity as normal
Detection sits at the 181-day global average

New · IoA

Indicators of Attack

Behavioural patterns caught in flight, before completion
Per-agent rolling baselines, not static signatures
Catches slow reconnaissance, tool-count drift, novel access
Picks up zero-day patterns that have no prior fingerprint
Compresses mean-time-to-detect from 96 hours to 9.9

From a blind window to a read you can act on

Before

247days

Damage accumulates the whole time, invisibly.

After

9.9hours

Mean time to detect, down from 96 hours.

Both

1stream

Threat detection and regulator-grade evidence, from one feed.

Before

247days

Damage accumulates the whole time, invisibly.

After

9.9hours

Mean time to detect, down from 96 hours.

Both

1stream

Threat detection and regulator-grade evidence, from one feed.

A blind 247-day window

Signature-era tooling only knows a breach happened once the indicators of compromise show up, on average 247 days later.

Before

247days

Damage accumulates the whole time, invisibly.

A blind 247-day window

Signature-era tooling only knows a breach happened once the indicators of compromise show up, on average 247 days later.

Every signal folds into one rolling baseline

Prompts, tool calls, file reads and API access are modelled together per agent. One fingerprint that every new interaction is measured against.

The same baseline the operator reads from

Risk vectors and automation-ready workflows surface from one feed: model usage, predictive risk, and the suggestions a compliance lead acts on.

Model usagePredictive riskAutomation candidates

Indicators of attack, in flight

Pattern 01low cumulative

Slow reconnaissance

An agent calls slightly more tools than its baseline over three days, then opens a config file it has never touched. Each step alone is benign. The sequence is not.

Pattern 02drift

Tool-count drift

A session opens 47 tool calls where the per-agent baseline sits at 12 ± 2. Every call resolves to an approved tool. The volume is the signal.

Pattern 03sudden

Novel endpoint access

A first-time hit on an API endpoint the agent has never used. The endpoint is approved at the firm level, but the access pattern is not, and it flags before completion.

Pattern 04off-baseline

Off-baseline file reads

Files outside the agent’s rolling read scope are touched in sequence. The semantic distance from the baseline is measurable, and ranks against thousands of past sessions.