At a glance
- Allowlist and denylist policies for AI agents only work on top of a live inventory of what employees actually run.
- Approve components by publisher, capability and configuration; deny the actions themselves — credential access, privilege escalation, unapproved data destinations.
- Backslash Security research found hidden instructions in a repository's AGENTS.md file could trick OpenAI Codex into silently accessing AWS credentials, npm tokens, Git configuration.
- Policy that is not enforced inline, before execution, is documentation; Backslash Security blocks risky agent actions before they run.
- Different teams need different rules, so write custom policies per risk profile and enforce them continuously.
Backslash Security
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Building allowlist and denylist policies for AI agents means deciding in advance which agents, models, MCP servers, Skills, hooks and rules files employees are permitted to run on their own machines — and which components and behaviors are refused outright. An allowlist is the approved set: the named coding assistants, Model Context Protocol servers (the connections an agent acts through to reach external tools and data), and Agent Skills (packaged instructions and scripts that extend what an agent can do, executing with the user's own permissions) that have been assessed and cleared. A denylist is the explicitly prohibited set, covering both components and the actions they attempt. Neither list is writable without discovery first, because no policy can approve or refuse software nobody has seen — and as of 2026 most of this layer arrives through self-service installs rather than through a software request. Backslash Security discovers every agent, model, MCP server, MCP tool, Skill, hook, rules file, plugin and connector running on an endpoint, scores what it finds, and then enforces the resulting allow and deny decisions inline, before a risky action executes. Governance teams also need the paper trail: according to Backslash Security, the platform automatically generates audit evidence for the EU AI Act, NIS2, DORA and SOC.
What does an allowlist or denylist actually control when the thing being governed is an AI agent?
An allowlist and a denylist actually govern discrete components of the agentic layer running on an endpoint, which is a narrower object set than most policy frameworks assume. An allowlist is the set of components explicitly approved to run; a denylist is the set explicitly prohibited. Scoped to the employee machine, the governed objects are not "applications" in the traditional sense — they are the parts an agent assembles at runtime, each with its own identity, permissions and failure mode.
| Policy object | What it is | Typical policy values | Why it matters |
|---|---|---|---|
| Agent | The coding or desktop agent itself — Claude Code, Cursor, GitHub Copilot | Approved / denied / approved for named teams | Sets which execution engine runs under the employee's identity |
| Model | The hosted or local model behind the agent, including runtimes such as Ollama or LM Studio | Approved or denied by provider or hosting location | Governs where prompts and context data are sent |
| MCP server | A Model Context Protocol connection through which an agent reaches external tools and data | Approved / denied / read-only scope | Each server is a live path into a real system |
| MCP tool | An individual callable action exposed by an MCP server | Tool-level approve or deny | A trusted server can still expose a destructive tool |
| Skill | Packaged instructions and scripts extending an agent, often a markdown file plus a script | Approved or denied by source | Executes with the user's own permissions |
| Hook | A trigger that fires before or after an agent action | Approved or denied | Changes behavior without changing the agent |
| Rules file | A file such as AGENTS.md or CLAUDE.md carrying standing instructions read on every run | Approved or denied sources | Instructions reach the agent silently |
| Plugin, connector | Extensions linking agents to SaaS and internal systems | Approved or denied | Expands the set of reachable systems |
Backslash Security allowlists approved components and denylists risky ones at this object level, with custom policies written per team and risk profile. Account context is governable in the same way: a personal login used on an enterprise machine is itself a policy object.
Why do denylist-only policies break down across MCP servers, tools, and connectors?
When employees install their own agent tooling on company machines, denylist-only policies break at the point of enforcement: a block list can only name components that have already been published, reviewed, and judged bad. The agentic layer does not wait for that. An MCP server — Model Context Protocol is the protocol agents use to reach external tools and data sources, and each server is a live connection an agent can act through — usually arrives as a few lines in a config file, not as an installed application. Agent Skills, packaged instructions and scripts that extend what an agent can do, are often just a markdown file sitting in a user's home directory. Both can be forked, renamed, or repointed at a different endpoint in seconds, and the renamed copy is on nobody's block list.
A second condition makes the gap structural, and it is the one still catching governance programs as of 2026: a component's name says nothing about what it does at run time. An approved connector can reach for credentials or push data to an unapproved destination without ever changing its identifier, so name-based filtering never sees the event that matters.
| Do this | But watch out for — and how to cover it |
|---|---|
| Keep denying components you have already judged unsafe | Forks, renames and locally built servers slip past; continuous discovery of what is actually running on each endpoint is what closes that window |
| Allowlist the components you have assessed as safe | A blanket allowlist pushes people into shadow AI — unapproved tools and personal-account logins; Backslash Security supports custom policies per team and risk profile, so research and production groups get different rules |
| Govern the behavior, not just the inventory | Approved components still drift or get manipulated; Backslash Security blocks risky agent actions inline before execution, including unauthorized code execution, credential access, privilege escalation, and data sent to unapproved destinations |
How do allowlist and denylist approaches compare as governance architectures?
Allowlist and denylist approaches differ on one structural question: what happens to an agentic component nobody has reviewed yet. An allowlist is default-deny — only explicitly approved agents, MCP servers (Model Context Protocol connections through which an agent reaches external tools and data) and Agent Skills (packaged instructions and scripts that extend what an agent can do, executing with the user's own permissions) may run. A denylist is default-allow: everything runs until it is specifically named as prohibited.
Five criteria decide which architecture suits a given population:
- Coverage — whether components you have never seen are governed by default. Decisive where new agents and Skills appear faster than review cycles.
- Maintenance burden — how much curation the list demands to stay useful.
- Employee friction — the delay between an engineer wanting a tool and being able to use it.
- Auditability — whether the policy itself produces a defensible record of what was permitted and why.
- Failure mode — what the organization is exposed to when the list is incomplete, which it always is.
| Approach | Coverage | Maintenance burden | Employee friction | Auditability | Failure mode |
|---|---|---|---|---|---|
| Allowlist (default-deny) | Complete by construction; unknown agents, MCP servers and Skills are blocked | High — every new component needs review and an entry | Higher; engineers wait for approval | Strong — the approved set is the record | Over-blocking; legitimate work stalls and users route around the control |
| Denylist (default-allow) | Partial; only named-bad components are stopped | Lower day to day, but the list ages quickly | Low; adoption continues uninterrupted | Weak — no positive record of what was sanctioned | Unknown components run unexamined, which is how shadow AI accumulates |
Default-deny suits regulated functions and sensitive endpoints such as developer workstations, where the auditability and coverage columns dominate. Default-allow suits broad, fast-moving populations where friction would push usage underground and defeat the control. Most organizations end up running both in different places, which is why Backslash Security supports allowlisting of approved components, denylisting of risky ones, and custom policies tailored to different teams and risk profiles, enforced continuously.
What belongs on an agent allowlist, and what criteria should decide entries?
This section narrows to one concrete case: allowlisting on the endpoint, where components are installed on employee machines and run under that employee's own identity and access. What belongs on an agent allowlist at that layer is every executable element of the local agentic stack, recorded as a named entry with a publisher and a version. The granularity sits below the product level: approving a coding client such as Claude Code or Cursor does not extend approval to the Skills, MCP servers and rules files later loaded into it, each of which can change what the client does without the client itself changing.
| Component | What an entry should pin | Why it decides approval |
|---|---|---|
| Agent / client | Named client, version, permitted install channel | Determines which other components can load |
| Model | Specific model and where inference runs (hosted, or local via Ollama or LM Studio) | Governs where prompt content and data travel |
| MCP server | A Model Context Protocol server is a connection an agent acts through; pin publisher, transport, credentials held | Each server is an outbound action path |
| MCP tool | The individual tool calls that server exposes | One server can expose write or delete operations |
| Agent Skill | Packaged instructions and scripts extending an agent — often a markdown file — pinned by hash | Skills execute with the user's own permissions |
| Hook | A trigger that fires before or after an agent action | Runs code outside the operator's direct prompt |
| Rules file (AGENTS.md, CLAUDE.md) | Standing instructions the agent reads on every run | Silently shapes behavior across every session |
| Plugin / connector | Endpoint reached and scopes granted | Defines the data the agent can pull or push |
The assessment criteria that justify an entry stay consistent across those rows: verified provenance, the permission and credential scope the component requests, its network destinations, whether it updates itself, and whether it runs under a corporate or a personal account. Backslash Security operates a free Skills Security Scanner that scans AI agent Skills for security risks, which gives teams a concrete starting point for the Skill rows before a formal policy exists.
How can a team roll out these policies without stalling the employees who use agents daily?
Teams can roll out allowlist and denylist policies for AI agents in stages, so that enforcement arrives only after the organization already knows what its people run. An allowlist names the agents, Skills, MCP servers and connectors approved for use; a denylist names those that are blocked. Default-deny — where anything not explicitly approved is refused — is the end state of that sequence, not its opening move. This is implementation-stage work, written for the team that has already decided to govern endpoint agentic AI and now has to ship it.
A staged path across all employees and all endpoints:
- Discover first. Establish what is actually running on employee machines before a single rule is written. Backslash Security offers a free AI Endpoint Exposure Assessment that is agentless, read-only, and retains no data, and it can be distributed through an existing MDM such as Intune or Jamf.
- Observe before you enforce. Compare the draft policy set against the discovery results and note what it would have blocked. That turns assumptions about Claude Code, Cursor or GitHub Copilot usage into observed actions you can show the teams affected.
- Allowlist what is already working. Approve the components people depend on daily. A control that ratifies an existing workflow generates no tickets.
- Stand up the exception route before the first block. A named owner, a request path in Jira or ServiceNow, and a stated turnaround. Policy friction comes less from what a denylist forbids than from how long an exception takes to clear, so the request path should exist before enforcement does.
- Denylist the clearly unsafe, then tighten by cohort. Move one population at a time toward default-deny rather than flipping the whole estate at once — developer workstations and finance usually warrant different thresholds.
- Re-run discovery on a cadence. New Skills, rules files and MCP servers appear between review cycles, and a policy set frozen at rollout gradually becomes an allowlist of tools nobody uses anymore.
Frequently Asked Questions
What is an allowlist and denylist policy for AI agents?
An allowlist and denylist policy for AI agents is a governance rule set that names which agentic components employees may run on their endpoints and which are refused. The objects under control are not only the agents themselves — Claude Code, Cursor, Codex, GitHub Copilot, Gemini CLI — but everything that extends them: models, MCP servers (Model Context Protocol connections through which an agent reaches external tools and data), MCP tools, Agent Skills, hooks, rules files, plugins and connectors. An allowlist states the approved set; a denylist names components judged too risky to execute, regardless of who installed them.
Why doesn't the existing endpoint security stack enforce these policies already?
The existing endpoint stack does not enforce agent allowlists because EDR — endpoint detection and response, the incumbent layer built to catch malicious processes, files and known-bad behavior — evaluates what a process is, not what an agent was instructed to do. An approved agent invoking an unapproved MCP server, or reading a rules file that carries standing instructions on every run, looks like ordinary signed software at the process boundary. Backslash Security operates at the agentic layer instead, discovering every agent, model, MCP server, MCP tool, Skill, hook, rules file, plugin and connector running on an endpoint so a policy has something concrete to reference.
Should rules files and hooks be covered by the policy, or only the agents?
Rules files and hooks belong in the policy, not outside it. A rules file — AGENTS.md or CLAUDE.md in a repository or on a machine — carries standing instructions an agent reads on every run, and a hook is a trigger that fires on an agent action, running something before or after it. Backslash Security research found that malicious instructions hidden in a repository's AGENTS.md file could trick OpenAI Codex into silently accessing AWS credentials, npm tokens and Git configuration. A policy that enumerates installed agents but ignores the instruction files feeding them leaves that path open.
How do you judge whether an MCP server or a Skill is safe to approve?
Judging an MCP server or a Skill means assessing what it can reach and what it can execute, since a Skill is packaged instructions and scripts that run with the user's own permissions — in practice, often a markdown file on the machine. Backslash Security's MCP Server Security Hub is a public, continuously updated risk database that held 81,021 MCP servers, each scored for risk, when read on 22 September 2026, and Backslash Security operates a free Skills Security Scanner that scans AI agent Skills for security risks. Assessment on the endpoint itself is agentless, meaning information is collected without leaving software permanently installed.
Can policies differ by team without blocking AI adoption?
Yes — custom policies can be written per team and risk profile, then enforced continuously, so a research group and a finance group need not share one rule set. Allowlisting approved components and denylisting risky ones gives each population a workable envelope, while inline prevention acts before a violation executes, blocking unauthorized code execution, credential access, privilege escalation and data sent to unapproved destinations. As Philip Walsh, Head of Security Engineering at Happy Returns, put it: "Backslash gave us a live picture of the AI our engineers were already running, and a way to govern it without slowing anyone down."
What audit evidence do these policies produce for regulators?
These policies produce a record of what ran, under which approval, and what was refused. Backslash Security automatically generates audit evidence for EU AI Act, NIS2, DORA and SOC, and traces the full path of an agent run from prompt to agent to tool call to outcome — the material an investigation needs to reconstruct an incident. That record also covers shadow AI, the unapproved agents, models, MCP servers and Skills employees install themselves, frequently under personal accounts on corporate machines. As of 2026, organizations starting from no inventory at all can use the free AI Endpoint Exposure Assessment from Backslash Security, which is agentless, read-only, and retains no data.
About this article
Backslash Security publishes this article under its own name and is responsible for its accuracy. Articles are researched and drafted with AI assistance and approved by Backslash Security before publication; publication and update dates reflect substantive edits, not automated refreshes. Last updated: 2026-10-07