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In Meta commercial terms changes slated to kick in on October 30, the company will shift the liability for any mistakes made by its AI models to its enterprise customers. The new language also makes those IT executives responsible for anything done by a partner or contractor.

The changes also finally put a stake into the heart of robots.txt, with Meta granting itself the right to grab, and use for its own purposes, any content associated with a Meta account.

The language allows Meta to “use automated software such as web scrapers, bots, robots, spiders, crawlers, and user agents and create test credentials to access, index, cache, analyze, collect, retrieve, or crawl the domains, web pages, or URLs, and any content available through them, that are associated with your products or services,” according to the new language on its website on Monday. Meta also wants to be able to “periodically pull updates from such domains, web pages, or URLs in order to provide, develop, improve, research, and maintain advertising and business products including business, commercial and related products to help you create and deliver ads and other commercial content.”

Consultants said the three biggest concerns they had about the changes were the web scraping, data ownership, and AI liability transfer.

Web scraping and data ownership

However, Meta emailed a statement to CIO.com late Monday suggesting that it was rethinking its position on web scraping. Saying that Meta wanted “to clarify” the web scraping changes, Emil Vazques, a Meta public affairs manager, said, “the crawling terms we consolidated only applied to our ads and business products and we’ve clarified that in the terms themselves. It does not change how Meta uses web crawlers. As always, content owners can use robots.txt to signal their preferences. This is not about scraping news sites, and these updates don’t give us new rights that supersede those preferences.”

As of Monday evening, New York time, the proposed document’s wording had yet to be altered to reflect this, but Vazques said that a change was coming soon.

The data ownership issue sets Meta apart from other AI vendors in that it wants to train it models on anything it encounters. Then again, the nature of Meta businesses focuses on advertising and would therefore be expected to seek more data control.

The new proposed phrasing, which Meta could alter before it takes effect later this month, said that using any Meta product grants Meta “a license to content that is covered by intellectual property rights like photos or videos you share, post, or upload on or in connection with our Meta Company Products. That license applies to content you or someone on your behalf, such as your agency that places an ad for you or your service provider that manages your Page content for you, makes available on or in connection with any Meta Company Products. Without limiting anything in our terms and for clarity, Meta may use such content … to provide, develop, improve, research, and maintain Meta Company Products.”

AI liability

But the change that prompted the most raised eyebrows involved AI liabilities. 

The proposed language said, “AI features may make mistakes and may not always produce accurate, complete, or suitable results,” and customers “are solely responsible for and assume any and all risks associated with such AI features and any resulting content.”

That AI clause, said Frank Dickson, principal analyst at Dickson Research, “turns accountability upside down. Meta builds the AI features, admits they ‘may make mistakes’ and then assigns ‘any and all risks’ to the business.”

Mike Wilkes, enterprise CISO at Aikido Security, agreed that the move is perplexing.

“The new AI provisions shift substantial responsibility for inaccurate or unsuitable outputs onto customers. That makes no sense,” he said. “As an example, the ASPCA is going to be liable for Meta creating unsuitable results when they ask Meta to create an image of a dog. Only Meta has control of the model being used to create that image. I see a very high flight risk among enterprise customers when platforms impose such onerous terms and conditions around content usage.”

The October 30 date that the US-based Meta chose is ironic. That is the day before Halloween, which is known as mischief night in the US, when children play pranks on their neighbors. Given that, Dickson said, the date for these changes is appropriate: “Meta is handing out the tricks and keeping the treats.” 

He added that the data grab is frightening. “What Meta wants is content: what a business posts, uploads, or shares on or in connection with Meta’s products,” he noted. “The trouble is the phrase ‘in connection with,’ which can stretch as far as Meta needs it to. The list of ‘Meta Company Products’ lives on a separate page that Meta can revise at will. The license grows every time that list does.”

And, Dickson noted: “Meta may use the content to ‘provide, develop, improve, research, and maintain’ its products. Those five verbs cover nearly everything a technology company does.”

‘Legal sophistry’

One of the obvious defensive measures for an enterprise is to sever all relations with Meta. But beyond the pragmatic difficulties of doing that, given the powerful market share enjoyed by Facebook, WhatsApp, Instagram and Threads, such a move may not even solve the problem. That is because Meta made other contract changes in July that make Meta licenses “perpetual” and that said, “this license survives termination of these terms by any party for any reason.”

That, said Noah Kenney, principal consultant at Digital 520, means that the “the general content license ends when content is deleted from Meta’s systems,” not when the enterprise ends a contract.

One additional twist to the new language is that Meta had recently tweaked it to no longer explicitly say that it would train it models on enterprise data. But Thomas Randall, a research director at Infotech, said that the wording change made by Meta amounted to legal sophistry. “The revised version drops the word ‘train’ and states that collection is just for developing and maintaining advertising and business products, alongside protecting Meta products’ integrity and security.”

But dropping the word “train” is “not a substantive fix,” he pointed out. “The revised terms are still flexible enough to cover most AI work. When combined with other aspects of the revised terms — continued use equals consent, broad indemnity, AS-IS disclaimer, and Meta’s freedom to discontinue products without notice or liability — this is a one-way street. The difference between ‘train Meta products’ and ‘developing and maintaining Meta products’ is a matter of how you pronounce tomato.”

Could force IT strategy changes

In addition, Aman Mahapatra, chief strategy officer for technology consulting firm Tribeca Softech, stressed that this contract could be an industry admission that robots.txt no longer delivers any meaningful protections. Agentic systems had already made robots.txt close to irrelevant. 

“October 30 ends a thirty-year assumption that robots.txt protects what you publish. It never did,” Mahapatra said. “It was a courtesy convention the web agreed to honor, and Meta’s own framing of robots.txt as a place for site owners to register preferences tells you exactly how it will be weighed against a signed contract.”

Mahapatra said the language in the new Meta commercial terms could force a significant change in IT strategy.

“‘On your behalf’ extends the license to whatever your agencies and vendors upload. ‘Associated’ extends the crawl to domains nobody has defined, including the ones where marketing pages sit beside product documentation, research, and investor materials,” Mahapatra said. “Most CIOs cannot tell me how many Meta pages, ad accounts, and WhatsApp numbers carry their brand, because marketing and agencies own them and IT never inventoried them.”

This article originally appeared on CIO.com.

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In olden days — last year, say — employees using unsanctioned AI tools might copy-and-paste sensitive data that could end up in the AI company’s training data set.

Today, an unsanctioned AI can hack into another company for an employee even if they didn’t explicitly ask it to.

This doesn’t just mean the latest unreleased frontier models undergoing security testing. Claude, ChatGPT, and Gemini have all evolved from being mere chatbots, answering questions and helping people write emails. They now have agentic capabilities and are able to carry out complex tasks on behalf of users.

And they do, sometimes to dangerous effect.

In August, ABC News in Australia interviewed a man identified only as “Andrew” who asked an AI agent to sign him up for a gym class. The agent hacked into the gym’s system and deleted someone else’s wait list reservation.

The AI was Anthropic’s Claude, and the agentic framework was OpenClaw.

Now imagine an employee asking their agentic AI assistant to help them make a sale. The request might inadvertently inspire the agent to hack into the prospect’s company.

To keep this — and other worst-case scenarios — from taking place, enterprises are building guardrails around their AI systems, restricting what data the AI has access to and what tools it’s allowed to use.

But none of those guardrails will work if the employee uses a tool that their company doesn’t know about.

“This is a problem we’ve long had,” says Matt Butkovic, technical director for cyber risk and resilience in the CERT division at Carnegie Mellon University’s Software Engineering Institute. “People will find ways to purchase or get access to things they think are useful, even when an organization says you probably shouldn’t do that.”

Ten years ago it was personal email and unauthorized cloud storage.

“The stakes are higher in some ways now,” adds Butkovic, who also teaches in CMU’s CISO program.

Verizon’s 2026 Data Breach Investigations report, released in May, shows that 67% of employees access AI services through non-corporate accounts on corporate devices. Shadow AI was the third most common non-malicious insider action detected in Verizon’s 2025 data loss prevention dataset.

And a May analysis of 1.9 million AI session minutes conducted by Harmonic Security found that when employees accessed personal accounts, 65% of their activity was related to business. They used personal accounts to write work emails and debug code, and even used it for legal work.

In a separate report, released in July, Harmonic reported that every organization the company monitored in the first half of 2026 had at least one employee sending data to a China-based AI tool. The uploaded data included M&A plans, source code, financial projections, and legal material.

To address this problem, enterprises are taking a fourfold approach:

• Figure out what employees want to use AI for and give them sanctioned, safe alternatives.
• Use security tools to monitor activity and keep employees from sharing sensitive data with unsafe systems, or even accessing them at all.
• Educate employees on the risks of AI and how to use it safely, because you can’t solve this problem with technology alone.
• Hold employees accountable when they violate AI policies.

Blocking DeepSeek at a financial services firm

BitGo, a Fortune 500 financial services firm with over $100 billion in digital assets under custody, uses a combination of strategies to combat unsanctioned AI.

“We have enterprise versions of Grok, ChatGPT, Claude and Gemini,” says Manny Khan, the company’s deputy CISO. “We are an AI-first organization. We encourage all of our people to use AI in their work, to try to automate as much of it as possible while keeping the human in the loop to avoid any slop.”

Unsanctioned AI is an industry-wide concern, he says, and having a sanctioned alternative is key.

“We encourage folks to use what is approved and has guardrails and harnesses and trust boundaries and safety checks,” he says.

To make the sanctioned alternatives easier and more appealing, employees use single sign-on, provided by Okta, to log into systems, and the enterprise-grade models also connect to the company’s MCP servers.

Once employees have a safe AI option to use, the next step is to get a handle on the unsafe ones.

For example, BitGo uses a data loss prevention tool from Cyberhaven. DLP was a useful security tool long before AI came along, but today it also helps ensure that sensitive data isn’t copied-and-pasted into untrusted chatbots.

“All traffic is proxied and analyzed by our security tools to identify anything potentially malicious, shut it down, and not allow the connection if it’s a site known to be malicious,” Khan says.

For example, the company blocks DeepSeek, a Chinese AI model. “We don’t want their models to train on our data,” Khan says.

But there’s only so much technology can do.

“Every site has AI now, and we cannot block every single site,” Khan says. “We might as well block the internet at that point.”

That’s where human awareness and education come in, says Khan.

“We do train people,” he says. “And we have an AI governance policy that we share company-wide and enforce this policy by tracking and measuring against it.”

It’s important for a company to have a single voice on AI use, a central figure with authority and ownership who can provide guidance, he adds. “Otherwise, you’ve got a lot of people who try to figure it out on their own. And that’s what you don’t want.”

Docusign on guard against agentic AI

“We want to educate users to do the right things, but people still want to experiment,” says Shivi Verma, senior manager of engineering at electronic signature firm Docusign.

That’s where it helps to have approved, secure tools for people to use, as well as systems in place to block the insecure ones.

“Some of the websites, like DeepSeek, we have blocked,” he says. “But we want to block in a way that focuses on education and focuses people on the right processes and tooling, which will drive people automatically to use approved tools.”

The company is also working on creating an allow list, he says, focusing on tools that employees can use without any concern.

And the company has network management tools like Zscaler, says Verma. “If people are posting confidential data in an external tool, it will give a popup, like a deterrent.”

Now, personal AI agents like OpenClaw are yet another evolution of the shadow AI problem, he says.

“We do monitor and people are using it, but we want to do it in a way that’s under security policies,” he says.

And with OpenClaw in particular, the danger is too high for just a warning.

“If it’s for personal use, that’s one thing,” Verma says. “But if you’re using it at work, your user has to be stopped immediately.”

Nonprofit builds routing system for approved AI

“Simply blocking AI tools is not a sustainable solution,” says Boris Kolev, global head of technology at JA Worldwide, a global educational nonprofit with almost 5,000 employees that serves more than 20 million people a year.

For JA Worldwide in particular, the problem was exacerbated by the fact that there was no approved solution until three months ago. As a result, employees got into the habit of using personal AI platforms.

“It was totally uncontrolled,” Kolev says. “Everyone who wanted to explore AI was using public models.”

At the beginning, people didn’t worry about what they shared and uploaded. “We tried to implement some policies, like ‘don’t upload corporate information.’ But it was hard to know what people were doing.”

Plus, AI was being embedded everywhere. On Windows. In people’s phones. “A user cannot avoid it,” says Kolev.

Then, earlier this year, the organization built a routing system, with enterprise-safe versions of Claude and ChatGPT running in private environments on AWS and Azure, in addition to some open-source models.

“Our colleagues can choose which model to use,” he says. “And sometimes they do a prompt two different times and choose which one works better.”

The system went into production in June, after a couple of months of testing and development.

“Now, after all the policies and rules are implemented, we have our own closed AI system, hosted on our own servers, so no risk of leaking corporate data,” he says. “But still, some projects are using public models, and we are trying to solve this problem.”

Another hitch: individual employees were often invested in the personal AI tools they’d grown comfortable with and reluctant to change to the corporate tools.

“Employees were saying, ‘I have so much history. It knows me, I cannot just switch,'” Kolev says.

And employees still have all the AI tools that come with their devices.

“This is hard to manage, and it’s getting even harder,” he says. There are monitoring and blocking tools available, but they’re too expensive. “We’re a nonprofit, and for us it’s almost impossible to afford monitoring tools. So we do a lot of educational sessions.”

The organization is also trying to make the sanctioned tools more appealing. That includes a mechanism to import the history and context from other platforms, he says. “And we’re trying to convince them that our tool works better and has more functionality, so we’re adding MCPs.”

MCP — model context protocol — allows an AI system to access data and tools directly. That includes connecting to corporate email and calendars, SharePoint, Monday.com, CRM, and all the other software that employees use for work, he says.

Kolev says that it’s a mistake to assume that employees use shadow AI because they don’t respect IT policies. It’s because IT didn’t move quickly enough to meet their needs.

“They find the AI faster than you can produce tools and governance and rules,” he says. “So my advice is that you start with simple rules that employees can understand, provide sanctioned tools, and continuously adapt governance.”

Holding employees accountable

According to an April survey of more than 1,200 employees conducted by Wakefield Research on behalf of PagerDuty, 66% of employees have used unsanctioned AI tools — and of those, 48% faced formal consequences, such as a warning or disciplinary action.

Holding people accountable is critical to a successful AI use policy, says Carnegie Mellon’s Butkovic. “We have to speak definitely about this in organizations,” he says. “If this behavior cannot be tolerated, then it shouldn’t be tolerated.”

Allowing the use of unsanctioned AI doesn’t just create risks but also opens up an organization to legal liability if the AI agents, acting on behalf of employees, harmed other companies or individuals.

“There could be consequential actions,” he says. Even if the employee responsible gets fired, “external parties that have been affected are still going to sue the company.”

The organization needs to be able to show that it has policies in place and enforces them when employees deliberately circumvent the policies.

“If you as an employee make a good-faith effort to comply with the policy, we’ll show you latitude if you’re not malicious,” Butkovic says. “But if you’re actively circumventing the system of controls in your organizations, that’s the case where there have to be swift and profound consequences.”

Related reading:

• 6 ways to make AI accountability stick
• AI policies work better when employees help write them
• Why AI adoption keeps outrunning governance — and what to do about it
• 5 ways to curb AI sprawl without stifling innovation

In Salesforce Agentforce, the recommended way to make an agent respond to events is a Flow: a record is created or a case is updated, and the Flow passes data into the agent's prompt. Separately, Email-to-Case and Web-to-Case turn inbound customer messages into case records; it is how airlines and hotels run support intake. Both are standard, documented, as-designed features. Chained together,

In Salesforce Agentforce, the recommended way to make an agent respond to events is a Flow: a record is created or a case is updated, and the Flow passes data into the agent's prompt. Separately, Email-to-Case and Web-to-Case turn inbound customer messages into case records; it is how airlines and hotels run support intake. Both are standard, documented, as-designed features. Chained together, [email protected]