Most leaders who feel behind on AI are not stuck because the tools are hard to use. They are stuck on one objection they keep hearing inside their own company: we cannot put our data into these tools. Legal says it. Security says it. The compliance team says it in a meeting and the pilot quietly dies.
On August 5, 2026, Anthropic announced a feature aimed squarely at that sentence. It is called inference hooks, and it lets a company's own security team inspect and gate what flows to Claude in real time, before the model ever sees a prompt or a tool result. It is available today in beta for Claude Enterprise customers.
For a business leader, what is worth understanding is what this changes about the conversation you have with your own risk owners.
What inference hooks actually do
Think of it as a checkpoint you control, sitting between your people and the model.
When the feature is turned on, every request to Claude routes through a signed connection to a security server your company runs. Before the model starts generating anything, Claude sends the prompt and its surrounding context to that server. The server returns a simple verdict: allow or deny. Claude only proceeds once it has one.
The same check applies in the other direction. When Claude calls a tool to fetch information, including tools connected through MCP, skills, and plugins, that tool's response is inspected before it is handed back to the model. So it watches both what your employee types and what your systems feed the model in the middle of a task.
The coverage includes Claude Enterprise surfaces such as chat, Claude Code, and Claude Cowork.
Why a security team pays attention is captured well by an early customer. Andrew Grimmett, Vice President of Information Security at Bandwidth, described it this way: "Inference hooks add a checkpoint to inspect what's flowing to Claude in real time, before anything sensitive leaves our environment. This lets us safely move faster on AI without giving up control."
That last clause is the whole point. Move faster without giving up control.
Move faster without giving up control.
Why this changes the "we can't use AI" conversation
For a long time, the two sides of the AI decision felt like they were pulling against each other. The reason to adopt AI was speed and capability. The reason not to was data exposure. Leaders were asked to pick one.
Inference hooks is the enterprise answer to that standoff made concrete. The control your compliance team wants and the adoption your business wants can now sit on the same system. When someone asks how the data stays safe, you have a real mechanism to point to instead of a promise.
It also connects to something your company most likely already owns. Inference hooks uses an open, webhook-based protocol built to work with existing data loss prevention systems. Anthropic names four compatible vendors by name: Netskope, Palo Alto Networks, Proofpoint, and Zscaler. Companies can also point it at a custom server they build themselves. If your security team already runs one of those platforms to watch data leaving your network, this extends that same discipline to your AI usage rather than asking them to trust a brand new black box.
The rollout controls a cautious executive will care about
The feature is built to be introduced carefully, which is the right instinct for anything touching sensitive data. A few of the controls named in the announcement:
- Shadow mode, which always allows the request through while still watching. This lets a team observe what the policies would catch before anything is actually blocked.
- Role-based exclusions, so you can apply different treatment to different groups.
- Percentage-based rollouts, so you can start small and expand as confidence grows.
Organizations can also tune failure-policy tolerance, timeouts, and other settings to match their own risk appetite. In the announcement's own words: "Simplify rollout with shadow mode (always allow), role-based exclusions, and percentage-based rollouts. Customize failure-policy tolerance, timeouts, and other settings to match your organization's risk tolerance."
The practical read: you do not have to flip a switch and hope. You can watch first, then enforce.
Where this sits in building an AI-capable team
In The 7 Levels of AI Proficiency, the early climb is about individuals learning to use AI well and check its work. The higher levels are about leaders building systems the whole organization can run inside safely. A control layer like this belongs to that upper part of the climb, the part where AI stops being a personal experiment and becomes something your company can govern.
Here is why that distinction is useful for you. Adopting AI is an organizational capability, more than a tooling decision. The companies that will pull ahead are not the ones with the flashiest demos. They are the ones whose leaders can answer the governance questions in the room. Knowing that a feature like inference hooks exists, and being able to name it when your CISO asks how prompts and tool responses get inspected, is exactly the kind of fluency that lets you say yes with confidence instead of stalling.
You do not need to be the person configuring the security server. You need to be the person who knows the control layer has arrived, so the next time a pilot gets blocked on data risk, you can route the conversation toward a real answer.
A few honest limits worth naming
Good decisions come from clear-eyed reading, so a few things the announcement does not tell us.
It is in beta, and no general availability date is given. No pricing is stated. And every request now waits on an allow-or-deny verdict from an external server before the model starts generating, which is a real design choice; the announcement puts no speed figure on it, so treat the performance question as open until you test it in your own environment. None of that undercuts the direction. It just means this is a capability to evaluate deliberately, not to assume is free or instant.
The next step
If a data-security concern is what is holding up an AI pilot in your company, bring this to your security team as a question, not a mandate: does our current data loss prevention setup work with a control like this, and would shadow mode let us watch before we enforce? That single conversation moves the decision from a flat no toward a workable yes. Start there.
Related reading: Level 6: The Admiral (Systems Integrator).
Sources
Frequently Asked Questions
Does inference hooks read what my employees type into Claude?
It inspects the prompt and its context before the model responds, and it inspects tool responses before they reach the model. The inspection is performed by a security server your own company runs, and it returns an allow or deny verdict. The design keeps the judgment in your hands rather than a vendor's.
Do we have to buy a new security platform to use it?
Not if you already run one of the named compatible systems. The feature works with existing data loss prevention servers and names Netskope, Palo Alto Networks, Proofpoint, and Zscaler as compatible, plus the option of a custom server.
Can we try it without blocking anyone yet?
Yes. Shadow mode always allows requests through while still watching, so a team can see what its policies would catch before turning on enforcement. Percentage-based rollouts let you expand gradually from there.
Is it generally available?
It is available today in beta for Claude Enterprise customers. The announcement does not give a general availability date or pricing.
Find your AI Proficiency level
The free 7 Levels assessment places you across seven stages of AI capability. Under ten minutes. Research-backed scoring.