Product

An identity-aware, policy-driven and auditable control point for enterprise AI use.

ByteGuard sits between your people and AI providers. It discovers AI use, establishes identity, inspects content, makes and enforces decisions, and records evidence.

AI Security Control Plane

One chain from discovery to evidence.

Discovers AI use, establishes identity, inspects content, makes and enforces decisions, and records evidence.

  1. 01Discover
  2. 02Identify
  3. 03Inspect
  4. 04Decide
  5. 05Enforce
  6. 06Audit

Core value proposition

On supported enforcement surfaces, prompts undergo identity and policy checks before reaching the provider.

Inline enforcement gateway

Real-time policy validation and identity context verification prior to egress.

  1. 01User + endpoint + application + provider account context
  2. 02Department- and playbook-based policies
  3. 03Decision lifecycle: ALLOW / BLOCK / QUARANTINE
  4. 04Immutable traces and an auditable decision chain

AI Identity Model

Same provider. Different policy. Decided by context.

The same AI provider can be subject to different security policies depending on the user and application context.

Identity context inputs

Endpoint
OS Session
Application
Surface
Provider Account
Department

Exact AI Identity

All dynamic context combined

Effective Policy & Playbook

Rules enforced in real time

Pre-provider Enforcement

Security depends on deciding before the provider sees anything.

ByteGuard holds the request, evaluates it and releases it only when policy allows.

Allow

The request is released once for this decision and continues to the provider.

Block

Nothing is submitted to the provider, and the decision is recorded as evidence.

Quarantine

The request waits for approval, so sensitive cases get a human decision.

Policy & Playbook Engine

Deterministic checks, a dedicated AI model and your connectors in a single decision chain.

  1. Organization Dictionary
  2. Detection & Rule Engine
  3. ByteGuard AI Security Model
  4. Enterprise DLP / Security Connectors
  5. Risk Aggregation
  6. Decision

ByteGuard AI Security Model

A dedicated analysis layer that assesses data leakage, security and contextual risks in AI use.

Defense in depth

The AI model works alongside deterministic rules, the organization's dictionary, DLP and risk thresholds to inform decisions.

Example

Why context matters

"Rewrite this as a board update: we are close to signing with the company we met in Ankara, the price is agreed, announcement after the quarter closes."

Pattern rules alone

No card numbers, no ID numbers, no keywords on a list. Nothing to match, so the prompt would pass.

Allowed

With the ByteGuard AI Security Model

Recognizes an unannounced deal and material non-public information in a leadership context. The policy routes it for approval.

Quarantined

Illustrative example. Outcomes depend on your policies and playbooks.

Prompt & File Security

Attachments are part of the decision, not a blind spot.

Documents submitted with a prompt can also be included in the policy decision.

Processing pipeline

  1. 1. AttachmentDocument input
  2. 2. ValidationType & signature
  3. 3. ExtractionText & OCR engine
  4. 4. Finding LocatorContext analysis
  5. 5. Policy DecisionEnforce rules

Supported file types & handling

PDF

Standard & secure PDF documents

  • Text & layout parsing
  • Embedded objects
  • Decryption check

Office

DOCX / XLSX / PPTX formats

  • Word documents
  • Excel spreadsheets
  • PowerPoint decks

Structured

TXT / CSV / JSON datasets

  • Raw text files
  • Tabular CSV data
  • JSON API payloads

Images

OCR scanning & text extraction

  • PNG / JPG / TIFF
  • Optical text recognition
  • Visual data extraction

Unresolved FAIL-CLOSED

Unsupported / encrypted files

  • Password protected
  • Unknown formats
  • Blocked by default

MCP, Tools & Skills Governance

Control the AI behind the AI.

Governance must cover both the LLM a user chooses and the resources that LLM can access.

ByteGuard AI Gateway

Visibility and policy for everything an agent can reach.

ByteGuard provides visibility into MCP server, tool and skill usage, with reporting and policy-based governance.

MCP Servers

Inventory · usage · block

Tools

Allow / block · risk · owner

Skills

Inspect · audit · remove unwanted

Projects

Workspace permission context

Example scenario

An unapproved MCP server appears in Claude Code

  1. DiscoveredA developer adds a new MCP server to Claude Code. ByteGuard adds it to the inventory with user, endpoint and project.
  2. EvaluatedThe server is not on the approved list for the developer's department, so policy marks it as not allowed.
  3. BlockedAccess to the server is blocked. The developer keeps working with the approved tools.
  4. ReportedThe event appears centrally with who, where, which server and why, ready for review.

Budget & Usage Management

Usage visibility and quota control for every AI provider.

ByteGuard reports AI usage per user, provider, account and department, and enforces quota-based policies on top of it.

Report and limit usage by

UserIndividual usage and personal quotas
ProviderUsage per AI vendor
AccountPersonal, team and enterprise provider accounts
DepartmentBudgets owned by each business unit
  1. 01Usage reports across users, providers, accounts and departments
  2. 02Quotas defined on any of these dimensions
  3. 03Quota-based policy actions: ALLOW / BLOCK and more

Auditable decisions & governance reports

Evidence for every decision, including why and where it was enforced.

Who

User · department

Where

Endpoint · app · surface

What

Prompt · file · tool

Why

Rule · AI model · connector

Decision

Allow · block · quarantine

Evidence

Event · immutable trace

See ByteGuard in your environment.

Book a walkthrough with our team. We will map your AI surfaces and show policy enforcement live.

Request a Demo Explore the Product