Should my enterprise AI agent do that? NanoClaw and Vercel launch easier agentic policy setting and approval dialogs across 15 messaging apps
Our take

For the past year, early adopters of autonomous AI agents have been forced to play a murky game of chance: keep the agent in a useless sandbox or give it the keys to the kingdom and hope it doesn't hallucinate a catastrophic "delete all" command.
To unlock the true utility of an agent—scheduling meetings, triaging emails, or managing cloud infrastructure—users have had to grant these models raw API keys and broad permissions, raising the risk of their systems being disrupted by an accidental agent mistake.
That tradeoff ends today. The creators of the open source sandboxed NanoClaw agent framework — now known under their new private startup named NanoCo — have announced a landmark partnership with Vercel and OneCLI to introduce a standardized, infrastructure-level approval system.
By integrating Vercel’s Chat SDK and OneCLI’s open source credentials vault, NanoClaw 2.0 ensures that no sensitive action occurs without explicit human consent, delivered natively through the messaging apps where users already live.
The specific use cases that stand to benefit most are those involving high-consequence "write" actions. That is, in DevOps, an agent could propose a cloud infrastructure change that only goes live once a senior engineer taps "Approve" in Slack.
For finance teams, an agent could prepare batch payments or invoice triaging, with the final disbursement requiring a human signature via a WhatsApp card.
Technology: security by isolation
The fundamental shift in NanoClaw 2.0 is the move away from "application-level" security to "infrastructure-level" enforcement. In traditional agent frameworks, the model itself is often responsible for asking for permission—a flow that Gavriel Cohen, co-founder of NanoCo, describes as inherently flawed.
"The agent could potentially be malicious or compromised," Cohen noted in a recent interview. "If the agent is generating the UI for the approval request, it could trick you by swapping the 'Accept' and 'Reject' buttons."
NanoClaw solves this by running agents in strictly isolated Docker or Apple Containers. The agent never sees a real API key; instead, it uses "placeholder" keys. When the agent attempts an outbound request, the request is intercepted by the OneCLI Rust Gateway. The gateway checks a set of user-defined policies (e.g., "Read-only access is okay, but sending an email requires approval").
If the action is sensitive, the gateway pauses the request and triggers a notification to the user. Only after the user approves does the gateway inject the real, encrypted credential and allow the request to reach the service.
Product: bringing the 'human' into the loop
While security is the engine, Vercel’s Chat SDK is the dashboard. Integrating with different messaging platforms is notoriously difficult because every app—Slack, Teams, WhatsApp, Telegram—uses different APIs for interactive elements like buttons and cards.
By leveraging Vercel’s unified SDK, NanoClaw can now deploy to 15 different channels from a single TypeScript codebase. When an agent wants to perform a protected action, the user receives a rich interactive card on their phone. "The approval shows up as a rich, native card right inside Slack or WhatsApp or Teams, and the user taps once to approve or deny," said Cohen. This "seamless UX" is what makes human-in-the-loop oversight practical rather than a productivity bottleneck.
The full list of 15 supported messaging apps/channels contains many favored by enterprise knowledge workers, including:
Slack
WhatsApp
Telegram
Microsoft Teams
Discord
Google Chat
iMessage
Facebook Messenger
Instagram
X (Twitter)
GitHub
Linear
Matrix
Email
Webex
Background on NanoClaw
NanoClaw launched on January 31, 2026, as a minimalist and security-focused response to the "security nightmare" inherent in complex, non-sandboxed agent frameworks.
Created by Cohen, a former Wix.com engineer, and marketed by his brother Lazer, CEO of B2B tech public relations firm Concrete Media, the project was designed to solve the auditability crisis found in competing platforms like OpenClaw, which had grown to nearly 400,000 lines of code.
By contrast, NanoClaw condensed its core logic into roughly 500 lines of TypeScript—a size that, according to VentureBeat, allows the entire system to be audited by a human or a secondary AI in approximately eight minutes.
The platform’s primary technical defense is its use of operating system-level isolation. Every agent is placed inside an isolated Linux container—utilizing Apple Containers for high performance on macOS or Docker for Linux—to ensure that the AI only interacts with directories explicitly mounted by the user.
As detailed in VentureBeat's reporting on the project's infrastructure, this approach confines the "blast radius" of potential prompt injections strictly to the container and its specific communication channel.
In March 2026, NanoClaw further matured this security posture through an official partnership with the software container firm Docker to run agents inside "Docker Sandboxes".
This integration utilizes MicroVM-based isolation to provide an enterprise-ready environment for agents that, by their nature, must mutate their environments by installing packages, modifying files, and launching processes—actions that typically break traditional container immutability assumptions.
Operationally, NanoClaw rejects the traditional "feature-rich" software model in favor of a "Skills over Features" philosophy. Instead of maintaining a bloated main branch with dozens of unused modules, the project encourages users to contribute "Skills"—modular instructions that teach a local AI assistant how to transform and customize the codebase for specific needs, such as adding Telegram or Gmail support.
This methodology, as described on NanoClaw's website and in VentureBeat interviews, ensures that users only maintain the exact code required for their specific implementation.
Furthermore, the framework natively supports "Agent Swarms" via the Anthropic Agent SDK, allowing specialized agents to collaborate in parallel while maintaining isolated memory contexts for different business functions.
Licensing and open source strategy
NanoClaw remains firmly committed to the open source MIT License, encouraging users to fork the project and customize it for their own needs. This stands in stark contrast to "monolithic" frameworks.
NanoClaw’s codebase is remarkably lean, consisting of only 15 source files and roughly 3,900 lines of code, compared to the hundreds of thousands of lines found in competitors like OpenClaw.
The partnership also highlights the strength of the "Open Source Avengers" coalition.
By combining NanoClaw (agent orchestration), Vercel Chat SDK (UI/UX), and OneCLI (security/secrets), the project demonstrates that modular, open-source tools can outpace proprietary labs in building the application layer for AI.
Community reactions
As shown on the NanoClaw website, the project has amassed more than 27,400 stars on GitHub and maintains an active Discord community.
A core claim on the NanoClaw site is that the codebase is small enough to understand in "8 minutes," a feature targeted at security-conscious users who want to audit their assistant.
In an interview, Cohen noted that iMessage support via Vercel’s Photon project addresses a common community hurdle: previously, users often had to maintain a separate Mac Mini to connect agents to an iMessage account.
The enterprise perspective: should you adopt?
For enterprises, NanoClaw 2.0 represents a shift from speculative experimentation to safe operationalization.
Historically, IT departments have blocked agent usage due to the "all-or-nothing" nature of credential access. By decoupling the agent from the secret, NanoClaw provides a middle ground that mirrors existing corporate security protocols—specifically the principle of least privilege.
Enterprises should consider this framework if they require high-auditability and have strict compliance needs regarding data exfiltration. According to Cohen, many businesses have not been ready to grant agents access to calendars or emails because of security concerns. This framework addresses that by ensuring the agent structurally cannot act without permission.
Enterprises stand to benefit specifically in use cases involving "high-stakes" actions. As illustrated in the OneCLI dashboard, a user can set a policy where an agent can read emails freely but must trigger a manual approval dialog to "delete" or "send" one.
Because NanoClaw runs as a single Node.js process with isolated containers , it allows enterprise security teams to verify that the gateway is the only path for outbound traffic. This architecture transforms the AI from an unmonitored operator into a supervised junior staffer, providing the productivity of autonomous agents without forgoing executive control.
Ultimately, NanoClaw is a recommendation for organizations that want the productivity of autonomous agents without the "black box" risk of traditional LLM wrappers. It turns the AI from a potentially rogue operator into a highly capable junior staffer who always asks for permission before hitting the "send" or "buy" button.
As AI-native setups become the standard, this partnership establishes the blueprint for how trust will be managed in the age of the autonomous workforce.
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As Ryan Donegan, who handles communications for Anthropic, put it in a press briefing: "Download the app and it uses what's already on your machine." Claude Dispatch turns your iPhone into a remote control for AI-powered desktop automation The real strategic play may not be computer use itself but how Anthropic is pairing it with Dispatch. Dispatch, which launched last week for Cowork and now extends to Claude Code, creates a persistent, continuous conversation between Claude on your phone and Claude on your desktop. A user pairs their mobile device with their Mac by scanning a QR code, and from that point forward, they can text Claude instructions from anywhere. Claude executes those instructions on the desktop — which must remain awake and running the Claude app — and sends back the results. 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Gagan Saluja, who describes himself as working with Claude and AWS, wrote: "combine this with /schedule that just dropped and you've basically got a background worker that can interact with any app on a cron job. that's not an AI assistant anymore, that's infrastructure." First hands-on tests reveal Claude's computer use works about half the time — and that may be the point Anthropic is calling this a research preview for a reason. Early hands-on testing suggests the feature works well for information retrieval and summarization but struggles with more complex, multi-step workflows — particularly those that require interacting with multiple applications. John Voorhees of MacStories, the Apple-focused publication, published a detailed hands-on evaluation of Dispatch the same day as the announcement. His results were mixed. Claude successfully located a specific screenshot on his Mac, summarized the most recent note in his Notion database, listed notes saved that day, added a URL to Notion, summarized his most recently received email, and recalled a screenshot from earlier in the session. But it failed to open the Shortcuts app on his Mac, send a screenshot via iMessage, list unfinished Todoist tasks (due to an authorization error), list Terminal sessions, display a food order from an active Safari tab, or fetch a URL from Safari using AppleScript. Voorhees' verdict was measured: Dispatch "can find information on your Mac and works with Connectors, but it's slow and about a 50/50 shot whether what you try will work." He added that it is "not good enough to rely on when you're away from your desk" but called it "a step in the right direction." Meanwhile, on GitHub, users are already surfacing technical issues. One bug report filed against Claude Code describes a scenario where the Read tool attempts to process multiple large PDF files in a single turn without checking whether the combined payload exceeds the 20MB API limit, causing the request to fail outright. The issue, which has been tagged as a bug specific to macOS, highlights the kinds of rough edges that come with shipping an early preview of a complex agentic system. OpenClaw, NemoClaw, and the startup swarm: Why Anthropic is racing to ship AI computer use now Anthropic's timing is not accidental. The company is shipping computer use capabilities into a market that has been rapidly reshaped by the viral rise of OpenClaw, the open-source framework that enables AI models to autonomously control computers and interact with tools. OpenClaw exploded earlier this year and proved that users wanted AI agents capable of taking real actions on their computers — and that they were willing to tolerate rough edges to get them. The framework spawned an entire ecosystem of derivative tools — what the community calls "claws" — that turned autonomous computer control from a research curiosity into a product category almost overnight. Nvidia entered the fray last week with NemoClaw, its own framework designed to simplify the setup and deployment of OpenClaw with added security controls. Anthropic is now entering a market that the open-source community essentially created, betting that its advantages — tighter integration, a consumer-friendly interface, and an existing subscriber base — can compete with free. Smaller startups are also pushing into the space. Coasty, which offers both a desktop app and browser-based AI agent for Mac and Windows, markets itself as providing "full browser, desktop, and terminal automation with a native experience." One user on social media directly pitched Coasty in the replies to Anthropic's announcement, claiming it offers "much better user experience and more accurate" results — a sign of how crowded and competitive the computer-use agent space has become in a matter of months. The competitive dynamics extend beyond just computer use. Reuters has reported that OpenAI is sweetening its pitch to private equity firms amid what the wire service described as an "enterprise turf war with Anthropic." The two companies are locked in an escalating battle for enterprise customers, and the ability to offer agents that can actually operate within a company's existing software stack — not just chat about it — is increasingly the differentiator. Prompt injection, screenshot surveillance, and the unsolved security risks of letting AI control your desktop If the competitive pressure explains why Anthropic shipped this feature now, the safety caveats explain why the company is hedging its bets. Computer use runs outside the virtual machine that Cowork normally uses for file operations and commands. That means Claude is interacting with the user's actual desktop and applications — not an isolated sandbox. The implications are significant: a misclick, a misunderstood instruction, or a prompt injection attack could have real consequences on a user's live system. Anthropic has built several layers of defense. Claude requests permission before accessing each application. Some sensitive apps — investment platforms, cryptocurrency tools — are blocked by default. Users can maintain a blocklist of applications Claude is never allowed to touch. The system scans for signs of prompt injection during computer use sessions. And users can stop Claude at any point. But the company is remarkably forthright about the limits of these protections. "Computer use is still early compared to Claude's ability to code or interact with text," Anthropic's blog post states. "Claude can make mistakes, and while we continue to improve our safeguards, threats are constantly evolving." The help center documentation goes further, explicitly warning users not to use computer use to manage financial accounts, handle legal documents, process medical information, or interact with apps containing other people's personal information. Anthropic also advises against using Cowork for HIPAA, FedRAMP, or FSI-regulated workloads. For enterprise and team customers, there is an additional wrinkle. Cowork conversation history is stored locally on the user's device, not on Anthropic's servers. But critically, enterprise features like audit logs, compliance APIs, and data exports do not currently capture Cowork activity. This means that organizations subject to regulatory oversight have no centralized record of what Claude did on a user's machine — a gap that could be a dealbreaker for compliance-sensitive industries. One user flagged this concern on social media with particular precision. NomanInnov8 wrote: "when the agent IS the user (same mouse, keyboard, screen), traditional forensic markers won't distinguish human vs AI actions. How are we thinking about audit trails here?" The question is not academic. As AI agents gain the ability to take real-world actions — sending emails, modifying files, interacting with financial systems — the ability to distinguish between human and machine actions becomes a foundational requirement for governance, liability, and compliance. Anthropic has not yet answered it. From excitement to anxiety: How users are reacting to Claude's new power over their machines The social media reaction to the announcement split roughly into three camps: those excited about the productivity implications, those concerned about the security risks, and those frustrated that they cannot yet use it. The enthusiasm was genuine and widespread. "Legit just got the update and used it with dispatch — exactly the feature I wanted," wrote one X user. Mike Joseph called the speed of Anthropic's feature releases "fantastic." Another X user noted the significance for non-technical users: "Very exciting for non-tech folks who don't want or know how to set up OpenClaw." But the security concerns were equally pointed. One user, posting as Profannyti, wrote: "Granting that kind of control over your personal device doesn't sit right. It's almost like letting someone you barely know take the wheel and trusting everything will be fine." As Engadget reported, experts have warned that one major concern with agentic AI is that "it can take major, sometimes dramatic actions quickly and with little warning," and that such tools "can also be hijacked by malicious actors." Several users flagged practical frustrations as well. Windows users — excluded from the macOS-only research preview — expressed predictable dismay. Others reported that the new features were consuming their usage quotas at alarming rates. One Max 20x subscriber paying $200 per month complained that Dispatch was "eating my quota like crazy," consuming 10% of their allowance in a single prompt. Another user linked to the GitHub bug report about the 20MB payload issue, calling the situation "quite urgent." Anthropic's enterprise playbook: Plugins, pricing tiers, and the bet that AI agents can replace entire workflows The pricing structure reveals where Anthropic sees the real market. While individual Pro users get access to Cowork, the company notes that agentic tasks "consume more capacity than regular chat" because "Claude coordinates multiple sub-agents and tool calls to complete complex work." Heavy users are nudged toward Max plans at $100 or $200 per month. For teams, the pricing starts at $20 per seat per month for groups of five to 75 users. Enterprise pricing is custom and includes admin controls to toggle Cowork on or off for the organization. The plugin architecture is where Anthropic's enterprise ambitions become clearest. Plugins bundle skills, connectors, and sub-agents into a single install that turns Claude into a domain specialist — for legal work, finance, brand voice management, or other functions. Anthropic already lists plugins for legal workflows (contract review, NDA triage), finance (journal entries, reconciliation, variance analysis), and brand voice (analyzing existing documents to enforce guidelines). The company is betting that the combination of computer use, Dispatch, scheduled tasks, and domain-specific plugins will create an agent capable enough to justify enterprise procurement. The testimonials Anthropic has gathered suggest the pitch is landing with at least some organizations. Larisa Cavallaro, identified as an AI Automation Engineer, described connecting Cowork to her company's tech stack and asking it to identify engineering bottlenecks. Claude, she said, returned "an interactive dashboard, team-by-team efficiency analyses, and a prioritized roadmap." Joel Hron, a CTO, offered a more philosophical framing: "The human role becomes validation, refinement, and decision-making. Not repetitive rework." The AI industry's defining tension: Shipping fast enough to win, slow enough to be safe Anthropic is shipping these capabilities at a moment of extraordinary velocity in the AI industry — and extraordinary uncertainty about what that velocity means. The company's own research quantifies the transformation underway. Its economic index, published in March 2026, tracks how AI is reshaping labor markets and productivity across sectors. The data suggests that AI adoption is accelerating unevenly, with knowledge workers in technology, finance, and professional services seeing the most dramatic shifts. Anthropic is also navigating significant external pressures beyond the product arena. Recent reporting has highlighted scrutiny from Senator Elizabeth Warren regarding Anthropic's defense and supply chain relationships — a reminder that the company's ambitions to build powerful autonomous agents exist within an increasingly complex political and regulatory environment. For now, the computer use feature remains early and imperfect. Complex tasks sometimes require a second attempt. Screen interaction is meaningfully slower than direct integrations. The audit trail gap for enterprise users is a genuine liability. And the fundamental tension between giving an AI agent enough access to be useful and limiting that access enough to be safe remains unresolved. But Anthropic is not waiting for perfection. The company is building in public, shipping capabilities it openly describes as incomplete, and betting that users will tolerate a 50 percent success rate today in exchange for the promise of something transformative tomorrow. It is a calculation that only works if the failures remain minor — a missed click, a stalled task, an unread email. The moment a failure isn't minor, the calculus changes entirely. The AI industry has spent the last three years proving that machines can think. Anthropic is now asking a harder question: whether humans are ready to let them act. The answer, for the moment, is a provisional yes — hedged with permissions dialogs, blocklists, and the quiet hope that nothing important gets deleted before the technology catches up to the ambition.
- OpenClaw has 500,000 instances and no enterprise kill switch“Your AI? It’s my AI now.” The line came from Etay Maor, VP of Threat Intelligence at Cato Networks, in an exclusive interview with VentureBeat at RSAC 2026 — and it describes exactly what happened to a U.K. CEO whose OpenClaw instance ended up for sale on BreachForums. Maor's argument is that the industry handed AI agents the kind of autonomy it would never extend to a human employee, discarding zero trust, least privilege, and assume-breach in the process. The proof arrived on BreachForums three weeks before Maor’s interview. On February 22, a threat actor using the handle “fluffyduck” posted a listing advertising root shell access to the CEO’s computer for $25,000 in Monero or Litecoin. The shell was not the selling point. The CEO’s OpenClaw AI personal assistant was. The buyer would get every conversation the CEO had with the AI, the company’s full production database, Telegram bot tokens, Trading 212 API keys, and personal details the CEO disclosed to the assistant about family and finances. The threat actor noted the CEO was actively interacting with OpenClaw in real time, making the listing a live intelligence feed rather than a static data dump. Cato CTRL senior security researcher Vitaly Simonovich documented the listing on February 25. The CEO’s OpenClaw instance stored everything in plain-text Markdown files under ~/.openclaw/workspace/ with no encryption at rest. The threat actor didn't need to exfiltrate anything; the CEO had already assembled it. When the security team discovered the breach, there was no native enterprise kill switch, no management console, and no way to inventory how many other instances were running across the organization. OpenClaw runs locally with direct access to the host machine’s file system, network connections, browser sessions, and installed applications. The coverage to date has tracked its velocity, but what it hasn't mapped is the threat surface. The four vendors who used RSAC 2026 to ship responses still haven't produced the one control enterprises need most: a native kill switch. The threat surface by the numbers Metric Numbers Source Internet-facing instances ~500,000 (March 24 live check) Etay Maor, Cato Networks (exclusive RSAC 2026 interview) Exposed instances with security risks 30,000+ observed during scan window Bitsight Exploitable via known RCE 15,200 instances SecurityScorecard High-severity CVEs 3 (highest CVSS: 8.8) NVD (24763, 25157, 25253) Malicious skills on ClawHub 341 in Koi audit (335 from ClawHavoc); 824 by mid-Feb Koi ClawHub skills with critical flaws 13.4% of 3,984 analyzed Snyk API tokens exposed (Moltbook) 1.5 million Wiz Maor ran a live Censys check during an exclusive VentureBeat interview at RSAC 2026. “The first week it came out, there were about 6,300 instances. Last week, I checked: 230,000 instances. Let’s check now… almost half a million. Almost doubled in one week,” Maor said. Three high-severity CVEs define the attack surface: CVE-2026-24763 (CVSS 8.8, command injection via Docker PATH handling), CVE-2026-25157 (CVSS 7.7, OS command injection), and CVE-2026-25253 (CVSS 8.8, token exfiltration to full gateway compromise). All three CVEs have been patched, but OpenClaw has no enterprise management plane, no centralized patching mechanism, and no fleet-wide kill switch. Individual administrators must update each instance manually, and most have not. The defender-side telemetry is just as alarming. CrowdStrike's Falcon sensors already detect more than 1,800 distinct AI applications across its customer fleet — from ChatGPT to Copilot to OpenClaw — generating around 160 million unique instances on enterprise endpoints. ClawHavoc, a malicious skill distributed through the ClawHub marketplace, became the primary case study in the OWASP Agentic Skills Top 10. CrowdStrike CEO George Kurtz flagged it in his RSAC 2026 keynote as the first major supply chain attack on an AI agent ecosystem. AI agents got root access. Security got nothing. Maor framed the visibility failure through the OODA loop (observe, orient, decide, act) during the RSAC 2026 interview. Most organizations are failing at the first step: security teams can't see which AI tools are running on their networks, which means the productivity tools employees bring in quietly become shadow AI that attackers exploit. The BreachForums listing proved the end state. The CEO’s OpenClaw instance became a centralized intelligence hub with SSO sessions, credential stores, and communication history aggregated into one location. “The CEO’s assistant can be your assistant if you buy access to this computer,” Maor told VentureBeat. “It’s an assistant for the attacker.” Ghost agents amplify the exposure. Organizations adopt AI tools, run a pilot, lose interest, and move on — leaving agents running with credentials intact. “We need an HR view of agents. Onboarding, monitoring, offboarding. If there’s no business justification? Removal,” Maor told VentureBeat. “We’re not left with any ghost agents on our network, because that’s already happening.” Cisco moved toward an OpenClaw kill switch Cisco President and Chief Product Officer Jeetu Patel framed the stakes during an exclusive VentureBeat interview at RSAC 2026. “I think of them more like teenagers. They’re supremely intelligent, but they have no fear of consequence,” Patel said of AI agents. “The difference between delegating and trusted delegating of tasks to an agent … one of them leads to bankruptcy. The other one leads to market dominance.” Cisco launched three free, open-source security tools for OpenClaw at RSAC 2026. DefenseClaw packages Skills Scanner, MCP Scanner, AI BoM, and CodeGuard into a single open-source framework running inside NVIDIA’s OpenShell runtime, which NVIDIA launched at GTC the week before RSAC. “Every single time you actually activate an agent in an Open Shell container, you can now automatically instantiate all the security services that we have built through Defense Claw,” Patel told VentureBeat. AI Defense Explorer Edition is a free, self-serve version of Cisco’s algorithmic red-teaming engine, testing any AI model or agent for prompt injection and jailbreaks across more than 200 risk subcategories. The LLM Security Leaderboard ranks foundation models by adversarial resilience rather than performance benchmarks. Cisco also shipped Duo Agentic Identity to register agents as identity objects with time-bound permissions, Identity Intelligence to discover shadow agents through network monitoring, and the Agent Runtime SDK to embed policy enforcement at build time. Palo Alto made agentic endpoints a security category of their own Palo Alto Networks CEO Nikesh Arora characterized OpenClaw-class tools as creating a new supply chain running through unregulated, unsecured marketplaces during an exclusive March 18 pre-RSA briefing with VentureBeat. Koi found 341 malicious skills on ClawHub in its initial audit, with the total growing to 824 as the registry expanded. Snyk found 13.4% of analyzed skills contained critical security flaws. Palo Alto Networks built Prisma AIRS 3.0 around a new agentic registry that requires every agent to be logged before operating, with credential validation, MCP gateway traffic control, agent red-teaming, and runtime monitoring for memory poisoning. The pending Koi acquisition adds supply chain visibility specifically for agentic endpoints. Cato CTRL delivered the adversarial proof Cato Networks’ threat intelligence arm Cato CTRL presented two sessions at RSAC 2026. The 2026 Cato CTRL Threat Report, published separately, includes a proof-of-concept “Living Off AI” attack targeting Atlassian’s MCP and Jira Service Management. Maor’s research provides the independent adversarial validation that vendor product announcements cannot deliver on their own. The platform vendors are building governance for sanctioned agents. Cato CTRL documented what happens when the unsanctioned agent on the CEO’s laptop gets sold on the dark web. Monday morning action list Regardless of vendor stack, four controls apply immediately: bind OpenClaw to localhost only and block external port exposure, enforce application allowlisting through MDM to prevent unauthorized installations, rotate every credential on machines where OpenClaw has been running, and apply least-privilege access to any account an AI agent has touched. Discover the install base. CrowdStrike’s Falcon sensor, Cato’s SASE platform, and Cisco Identity Intelligence all detect shadow AI. For teams without premium tooling, query endpoints for the ~/.openclaw/ directory using native EDR or MDM file-search policies. If the enterprise has no endpoint visibility at all, run Shodan and Censys queries against corporate IP ranges. Patch or isolate. Check every discovered instance against CVE-2026-24763, CVE-2026-25157, and CVE-2026-25253. Instances that cannot be patched should be network-isolated. There is no fleet-wide patching mechanism. Audit skill installations. Review installed skills against Cisco’s Skills Scanner or the Snyk and Koi research. Any skill from an unverified source should be removed immediately. Enforce DLP and ZTNA controls. Cato’s ZTNA controls restrict unapproved AI applications. Cisco Secure Access SSE enforces policy on MCP tool calls. Palo Alto’s Prisma Access Browser controls data flow at the browser layer. Kill ghost agents. Build a registry of every AI agent running. Document business justification, human owner, credentials held, and systems accessed. Revoke credentials for agents with no justification. Repeat weekly. Deploy DefenseClaw for sanctioned use. Run OpenClaw inside NVIDIA’s OpenShell runtime with Cisco’s DefenseClaw to scan skills, verify MCP servers, and instrument runtime behavior automatically. Red-team before deploying. Use Cisco AI Defense Explorer Edition (free) or Palo Alto Networks’ agent red-teaming in Prisma AIRS 3.0. Test the workflow, not just the model. The OWASP Agentic Skills Top 10, published using ClawHavoc as its primary case study, provides a standards-grade framework for evaluating these risks. Four vendors shipped responses at RSAC 2026. None of them is a native enterprise kill switch for unsanctioned OpenClaw deployments. Until one exists, the Monday morning action list above is the closest thing to one.