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New category for secure AI development

Agent Access Security Broker

The governance layer for AI coding agents. Unbound helps enterprises discover AI coding agents, understand their risk, and enforce guardrails over what those agents can see, touch, and do.

Discover tools, MCP servers, sub-agents, and risky settings
Monitor terminal commands and MCP actions as work happens
Enforce policy without forcing developers to abandon their tools

Works across the tools your teams already use

CursorCursor
Claude CodeClaude Code
ClineCline
Roo CodeRoo Code
Gemini CLIGemini CLI
CodexCodex
GitHub CopilotGitHub Copilot
WindsurfWindsurf

Plus any tool that runs in the terminal or connects via MCP.Deploy via Jamf, Intune, JumpCloud, or Kandji.

AI coding agents changed the security question

This is no longer just about which model a developer prefers. It is about what the agent can do with enterprise permissions: edit files, run terminal commands, call MCP tools, query internal systems, and take multi-step actions in real time.

Destructive terminal commands
Unsafe MCP server actions
Production data changes
Secrets pushed to remote tools
Over-permissive auto-approve
Shadow agent & connector sprawl
The Category

What is an Agent Access Security Broker?

An AASB is the control layer between AI coding agents and the tools, systems, files, and actions they can access. It creates visibility, risk analysis, and policy enforcement for agent access across your terminals, MCP servers, apps, & data.

If CASB secured employee access to cloud apps, AASB secures agent access to tools, files, systems, and actions.

Discover

Inventory agents, MCP servers, sub-agents, prompt/policy rules, and risky settings.

Assess

Surface risky autonomy, unsafe permissions, dangerous connectors, and high-risk usage patterns.

Enforce

Audit, warn, block, or require approval for risky actions and sensitive data flows.

The Gap

Why existing controls are necessary but not enough

AppSec, IAM, endpoint controls, and AI gateways still matter. The problem is that none of them were built to govern live agent behavior inside coding workflows.

Existing ControlWhat It Does WellWhere It Falls Short
AppSec / Code ScanningFinds vulnerabilities and policy issues in code artifactsDoes not govern live terminal commands, MCP tool invocations, or agent permissions while work is happening
IAM / PAMControls identities, credentials, and privileged account accessDoes not understand agent autonomy settings, sub-agents, MCP servers, or in-session agent intent
EDR / EndpointSees processes and device activityCannot explain why the agent acted, which policy should apply, or whether the action was agent-approved vs. user-approved
AI GatewayRoutes and secures model trafficMay not see IDE posture, terminal behavior, MCP actions, or risky configuration states inside coding tools
CASB / DLPGoverns SaaS access and some data movementNot designed for IDE/CLI workflows, agent permissions, or approval logic around live coding-tool actions

A new control layer is needed built specifically for AI coding agent governance.

The Risk

What happens if you do not solve for AASB?

Without a dedicated control plane, organizations inherit a compound failure mode: more autonomy, more connectors, more hidden configurations, and less visibility into what is happening in real time.

Hidden AI sprawl

Security cannot confidently answer which agents, versions, MCP servers, and rule sets are in use.

Excessive autonomy

Auto-approve, unsafe allowlists, and broad permissions let agents act faster than reviewers can react.

Connector risk

Unsanctioned MCP servers and external tools expand the blast radius beyond the IDE.

Runtime risk

Destructive commands or sensitive operations happen before post-commit or after-the-fact controls can help.

Compliance gaps

Investigations and compliance reviews slow down when there is no reliable record of actions, approvals, and policy outcomes.

Productivity backlash

Security falls back to blanket restrictions, and developers route around them.

The Solution

How Unbound operationalizes AASB

Unbound gives security and engineering a shared control plane across discovery, posture, runtime governance, analytics, and rollout.

Agent Discovery

Discover the real estate

Inventory AI coding tools, MCP servers, sub-agents, agent rules, and risky configurations across the organization.

  • Detect Cursor, Claude Code, Copilot, Cline, and 20+ tools
  • Enumerate MCP servers and their configurations
  • Map agent rules and extension inventory
  • Track installation drift over time
AI Tools DiscoverySummary
AI Tools DistributionView details ›
100%AI Tools
Cursor (11%)11
Claude Code (9%)9
Gemini CLI (7%)7
Roo Code (Cursor) (7%)7
Roo Code (VS Code) (6%)6
Copilot Chat (VS Code) (6%)6
Cline (Cursor) (5%)5
Copilot (VS Code) (5%)5
Cursor CLI (5%)5
OpenClaw (4%)4
IntelliJ IDEA (4%)4
Devices with most coding tools
2
20
3
15
5
14
7
12
9
9
6
7
12
6
012
No of Devices
Users with the Tools
User NameNo Of ToolsTools Installed
V
Vignesh Subbiah
9
CursorGemini CLIRoo Code (Cursor)+6 more
N
Nanda Pranesh
3
CursorClaude CodeCodex
R
Rajaram Srinivasan
7
CursorGemini CLIClaude Code+4 more
K
Kirnesh Nandan
5
Roo Code (Cursor)CursorAntigravity+2 more
R
rajarams
3
CursorGitHub Copilot (VS Code)Copilot Chat
G
Gowshik T
14
Kilo CodeGemini CLIOpenCode+11 more
AI Tools DiscoveryPermissions
Add Filters
Autonomy Levels
13
Autonomy LevelUsers
Ask First (54%)7
Auto Edit (31%)4
Full Auto (15%)2
Top Risk Factors
Command Execution
Network Access
No Deny Rules
Auto Edit
File Protection
01234567
No of users
Top Risky Users
sumit
Gowshik T
Nanda Pranesh
Vignesh Subbiah
Pugazhendhi M
012345678910
Risk Score
Users by Risk Level
Search user
UserToolAutonomyRisk FactorsScore
G
Gowshik T
Cursor
Full Auto
Full AutoCommand Execution+3
⚠⚠9
S
sumit
Claude Code
Full Auto
Full AutoCommand Execution+4
⚠⚠9
N
Nanda Pranesh
Cursor CLI
Auto Edit
Auto EditCommand Execution+3
7
V
Vignesh Subbiah
Claude Code
Auto Edit
Auto EditCommand Execution+2
6
G
Gowshik T
Claude Code
Auto Edit
Auto EditNetwork Access+3
6
P
Pugazhendhi M
Claude Code
Auto Edit
Auto EditCommand Execution+3
6
Risk Assessment

See live agent behavior

Monitor terminal runs and MCP actions by user, application, and tool while work is happening. Benchmark against peer organizations.

  • Per-developer security posture scores
  • Risky MCP server connection alerts
  • Autonomy and permission risk analysis
  • Trend tracking and drift detection
Policy Engine

Enforce guardrails where risk happens

Audit, warn, block, or require approval for destructive commands, unsafe MCP use, unsanctioned tools, and sensitive data movement.

  • Terminal command allow/deny with semantic parsing
  • MCP server connection and action policies
  • Approval workflows for high-risk operations
  • Full audit log of every agent action
Tool Policies
Tool Policies
Policy Health
Environment Targets
Search policy name, description...
Filter
Name TypeCommand Family / MCP ServerTarget PatternActionStatus
Figma Block Whomi
MCP Action
figma-remote-mcp
whoamiAuditActive
Atlassian UserInfo
MCP Action
atlassian
get.JiraIssueAuditInactive
Linear
MCP Action
linear
list_usersAuditInactive
Sentry Find Projects
MCP Action
sentry
find_projectsAuditInactive
Slack
MCP Action
slack
slack_search_usersBlockInactive
Notion create comment
MCP Action
notion
notion-create-commentAuditInactive
Github Search Code
MCP Action
github
search_codeAuditInactive
sentry block
MCP Action
sentry
All Tools (*)BlockInactive
Block Data Exfiltration
Terminal Command
data_transfer
*AuditActive
Audit File Write
Terminal Command
write_file
*env*AuditInactive
Deployment

Works with the tools teams already use

Unbound meets developers where they already work. Teams can start in visibility mode, then move to warnings, approvals, and enforcement as policy matures.

1

Connect & Discover

Connect or discover the AI coding tools already in use across your engineering org.

2

Baseline & Assess

Baseline risky configurations, MCP servers, and user patterns. Score posture against benchmarks.

3

Progressive Policy

Turn on progressive policies: audit first, then warn, approve, or block as confidence grows.

Frequently asked questions

Common questions about the AASB category and Unbound.

No. An AI gateway helps route and secure model traffic. An AASB focuses specifically on the live access problem created by AI coding agents: what tools, systems, files, and actions an agent can reach and whether that behavior is allowed.
They remain essential, but they were not built to govern in-session agent behavior, tool calls, MCP connectivity, or risky autonomy settings inside coding workflows.
No. Unbound is designed for heterogeneous environments and can govern multiple tools while the organization standardizes over time.
Yes. The recommended adoption path is discovery and audit first, then warnings and approvals, then blocking for high-confidence policies.
Use guardrails to detect secrets, PII, regex and keyword matches, document classification, and route or block content. Custom guardrail webhooks can extend policy using existing security systems.
No, your existing workflows for SAST and DAST already sufficiently cover code security and vulnerabilities arising from AI-generated code.

Make AI coding agent adoption governable

Start with a governance assessment or see the discovery dashboard to align security and engineering on the AASB model.

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