Why Blanket AI Bans Backfire and Fuel Shadow AI

Wade Foster Zapier’s CEO warns that strict AI bans Framework of No backfire. Employees will keep using familiar AI tools on personal accounts, hiding their activities from security.
This matters because shadow AI unsanctioned use of generative AI creates blind spots: data leaks and incidents go undetected without governance.
The unintended outcome of blanket no policies is more risk: unhappy users turn to unmanaged tools, increasing data exposure and compliance gaps.
Key takeaway: Security teams should shift from blanket bans to managed AI risk. Provide approved tools, set guardrails, and monitor usage to close security visibility gaps rather than pushing innovation underground.

In a January 2026 SANS blog summarizing a talk on the Agents of Scale podcast, Wade Foster, CEO of Zapier, made waves by arguing that a deny by default approach to AI actually increases shadow AI. He observed that telling employees no simply pushes them to use personal AI tools out of security’s view. As he bluntly put it, When security says no, employees do not stop using AI. When an incident happens, you have no map of which AI systems were involved.. This insight reframes the discussion: it’s not that AI tools themselves are inherently insecure, but rather that a lack of sanctioned channels drives risky behavior.

Shadow AI refers to exactly that: employees using AI tools like ChatGPT, code assistants, chatbots, or browser extensions without formal approval or oversight. It’s essentially a subset of the classic Shadow IT problem, but with generative AI. Shadow AI is occurring now because AI has become ubiquitous and powerful, and business teams are hungry to use it. Analysts warn that by 2027, roughly 75% of employees will use technology outside IT’s visibility. For security leaders, this makes the topic urgent. If security teams pretend shadow AI doesn’t exist, they risk blind spots in compliance, data protection, and incident response. Wade Foster’s remarks crystallize why this is a critical conversation: No has unintended consequences in practice, and understanding them is key as organizations grapple with enterprise AI governance challenges.

What Was Meant And Why It Resonates

Foster’s core argument is that universal denial of AI is counterproductive. He calls a deny by default security posture the Framework of No. Under this framework, any new AI tool or feature is assumed forbidden unless someone proves otherwise. On paper it sounds safe, but in reality it stalls legitimate use. Foster points out that many organizations have precisely this experience: when official AI projects are blocked or delayed, employees simply use consumer tools on their own. As one SANS summary notes, workers turn back to familiar AI apps on personal accounts and devices when corporate tools are locked down. They stop asking permission because they already know the answer will be no. In essence, a blanket now drives employees to innovate in the shadows.

This message resonates with security practitioners because they’ve seen shadow AI growing. Foster emphasizes that AI is already part of workflows across thousands of organizations, and controls haven’t caught up. The choice, he says, is between managed AI risk and unmanaged AI risk. When leaders forbid AI, employees still find ways to use it, but without any governance or monitoring. That means faster but chaotic deployments in a dynamic underground ecosystem as one cybersecurity expert described it. Foster’s insight rings true for many: even if security champions say they have an AI policy, if it’s just words on paper and no sanctioned tools, staff will solve problems their own way. So the core idea that forbidding AI actually fuels shadow AI is one shared by others in the field, which is why Foster’s comments are gaining attention.

Why Just Saying No Backfires in Security

In practice, a security policy that simply denies AI use creates friction that pushes workarounds. When official channels are blocked, employees with pressure to deliver results will bypass them. For example, if marketing or development needs an AI assisted solution quickly and corporate IT blocks ChatGPT or Copilot, they might use a personal ChatGPT account or a rogue browser extension instead. This friction to bypass behavior is well documented from older Shadow IT cases. Palo Alto Networks describes shadow AI as employees adopting generative AI on their own, often innocently trying to get work done faster because sanctioned tools are slow, limited, or unavailable. In short, if you make something forbidden or hard to access, people will find a way around it.

Another factor is the speed of AI innovation. Security teams often move slowly through committees and risk reviews, whereas AI tools evolve monthly. Wade Foster noted that just in a few months AI models leaped from GPT 4 to GPT 5 capabilities, while corporate AI policies were still catching up to GPT 3 era technology. In reality, businesses can’t afford to wait for years-long approvals while competitors harness new AI productivity gains. As a result, employees skip the slow process. They might write code faster with a consumer LLM, summarize reports with an unsanctioned chatbot, or use AI driven CRM plugins that IT hasn’t approved. By the time security catches up, the official project approval is moot because employees have already adopted something else informally.

The net effect is a severe loss of visibility. Any AI usage outside sanctioned platforms means no security logging, no data loss prevention DLP, and no centralized oversight. Palo Alto’s Cyberpedia notes that when shadow AI tools are used informally, they aren’t covered by enterprise security, governance, or compliance controls. In practical terms, that might mean sensitive data is entered into a public chatbot which could log or leak it without any firewall or DLP alert. IBM’s 2025 data breach report even highlighted AI related incidents averaging over $650,000 in costs. In other words, saying no to AI on the security team’s terms doesn’t stop the tool, it just hides it, making incidents harder to detect and far more damaging when they occur. Wade Foster puts it succinctly: without sanctioned use, when an incident happens, you have no map of all the AI systems that were involved. This visibility gap is precisely what makes a just say no approach backfire.

Shadow AI as a Security Visibility Problem

It’s important to reframe the issue: Shadow AI isn’t inherently an AI flaw, but a governance and visibility gap. This mirrors the old Shadow IT challenge. In the past, enterprises banned unauthorized cloud apps or external drives, only to find employees using personal Gmail, Dropbox, or SaaS services anyway. Those policies often had to shift toward offering safe alternatives e.g. enterprise Google Workspace. Shadow AI is the modern equivalent. F5 Labs even describes Shadow AI as a fast developing offshoot of Shadow IT. Both involve workers solving real problems with outside tools because official ones aren’t meeting their needs.

Like Shadow IT before it, Shadow AI adds a new layer: AI tools can ingest and transform sensitive data in unpredictable ways. For instance, F5 gives examples of engineers pasting proprietary code into ChatGPT or staff uploading confidential memos to a public AI service. These actions create unmonitored information flows out of the organization. If the security team isn’t aware this is happening, it’s just a big blind spot. The root cause is the same as in Shadow IT: the lack of sanctioned, well governed alternatives and the inability of security to monitor the endpoint of innovation.

This perspective is crucial for security teams. It highlights that the real problem isn’t the AI itself, but the loss of visibility and control over how tools are used. Instead of framing shadow AI as a battle of good vs. evil, it helps to see it as the symptom of misaligned policy. Security teams won’t fix it by pretending the genie is back in the bottle, they fix it by bringing AI use into an observable, managed framework. In that sense, the answer comes from strengthening governance inventorying tools, logging usage, educating users, not simply outlawing new technology.

Where Security Teams Go Wrong

Security teams often make understandable but counterproductive mistakes in this domain. First is the blanket ban, a policy that says no employees may use ChatGPT or any LLM on work. This may feel like a safe default, but it ignores context. Not all AI tools carry equal risk, and not all use cases are sensitive. By lumping everything together, security creates an all or nothing game. This is the Framework of No in action, and it fails for reasons we’ve seen: employees will either ignore it or stall productivity. Industry voices echo this experts say bans simply don’t work and in fact drive AI use further underground. Security teams thus suppress innovation and trust without actually preventing risk.

Another mistake is having policy without enablement. Some teams write strict AI guidelines but offer nothing in return, no internal AI assistant, no vetted tools, just a rulebook. This creates a vacuum. Wade Foster calls it half enablement: allowing certain capabilities only partially. His warning: half enabling actually increases frustration and shadow AI more than no enablement. For example, if the company provides access to a limited AI tool or only to certain departments, other users will feel left out and be tempted to find their own solutions. This disjointed approach signals to employees that security is a roadblock, not a partner.

A third common error is ignoring business velocity. Security processes that took years for classic IT now feel glacial in the AI era. If policy still revolves around lengthy RFPs and deep audits, the business sees that as a barrier to staying competitive. Meanwhile, a neighbor’s team may already be shipping AI powered features with a personal tool. If security can’t keep pace for example, if it’s still debating a policy for ChatGPT while generative coding assistants are building half your product teams will move ahead without it. This disconnect where the business demands AI level productivity while security maintains pre-AI policies only widens the shadow gap.

Lastly, many security cultures inadvertently punish curiosity. When early AI experimenters in the company report their success stories or even failures, they need safe feedback channels. Instead, if the response is I told you no or worse, blame you for breaking policy, people go silent. Foster notes that employees worried about getting their hand slapped for AI tests will hide both wins and failures. Security teams going this route miss learning opportunities and leave risk unaddressed. The common thread in these missteps is a lack of trust and collaboration: teams either push users away or leave them unsupported, which only accelerates shadow activity.

What Actually Works Better

The alternatives to blunt bans involve enabling safe AI use with appropriate checks. In practice, this means building guardrails rather than walls. One clear strategy is to pre approve a small set of AI tools or platforms that meet security and compliance standards. Wade Foster’s own approach at Zapier was instructive: he convened legal, security, and procurement teams and said, We got to go figure out how to greenlight purchasing of a handful of these tools and make sure they fit within our policy framework. In short, identify a few high value AI services like a corporate ChatGPT with enterprise privacy settings, an internal AI assistant, or vetted code generation tools and give people a sanctioned way to use AI. When employees know which tools are ok, they’re less tempted to stray.

Along with approved tools, it’s important to educate and monitor. Formal AI governance should include clear policies about what data can go into AI, what use cases are allowed, and how outputs must be reviewed. F5 Labs recommends exactly this: Establish a clear AI usage policy that defines approved tools and data handling, and maintain a vetted list of AI tools with built in protections. In parallel, companies should train teams on AI risks from data leakage to hallucinations so staff understand why policies exist. Monitoring plays a role, too. As Wade advises, security should let people experiment but keep watch. For example, applying DLP on enterprise chat logs or analyzing network traffic can surface unauthorized AI traffic. F5 specifically says to monitor, then adapt and to respond with education rather than punishment when shadow AI is spotted. The goal isn’t to catch and blame, but to learn where gaps are and improve governance.

In essence, the high level strategy is to shift mindsets, treat AI as an innovation you’ll harness securely, not a forbidden fruit. Security teams should move to default yes with justification for no. Rather than building an ivory tower, they become collaborators. For example, they might offer sandbox environments or APIs that channel data to approved AI models, giving business units the power they need within controlled bounds. They might implement just in time reviews allowing a tool for 30 days while conducting an expedited risk assessment instead of decades long procurement. These approaches mean some risk is allowed, but crucially it’s managed and visible. The payoff is organizations get the AI productivity they need and stay in the loop.

What This Means for CISOs and Security Leaders

For security leaders, Foster’s argument implies a shift in posture. First, understand that AI risk is here to stay, eliminating it entirely is a fantasy that can drive problems underground. Foster frames it as a choice: embrace managed risk or be plagued by unmanaged risk. CISOs should therefore aim to be enablers of safe AI, not outright blockers. This means adjusting the risk appetite and controls: they might need to accept some acceptable data exposure in AI testing with contracts that limit vendor data use in exchange for reducing secret usage on uncontrolled platforms.

Governance mindsets also need to evolve. Traditional castle and moat security fully centralized, preventing anything new may falter. Gartner’s research cited by F5 predicts that static, centralized models will fail as 75% of tech use will be outside IT’s direct oversight by 2027. Instead, CISOs should create a federated or collaborative model: cross functional AI committees, clear processes for adding new tools, and embedded security champions in business units. In practice, trust and communication become as important as technical controls. As Foster notes, if employees trust security to give guidance rather than punishment, they will share insights on what they’re trying to solve. That trust builds resilience: teams learn from each other’s AI projects and improve them with oversight, instead of hiding successes.

Finally, this has organizational implications. Security leaders must advocate for transparency around AI use. They need to invest in tooling like monitoring or data classification that gives them visibility into where AI might touch data. They may revise incident response plans to include AI tools as possible components. At a high level, it means reframing the narrative: instead of warning everyone that AI is dangerous, stay away, communicate, we’ll give you the tools and practices to do AI safely. By doing so, CISOs help preserve agility and innovation while still protecting the company’s assets and reputation. As Foster concludes, the companies that figure out this balance will outperform the ones stuck in denial.

FAQs

Is Shadow AI inevitable?

Not inevitability in the sense that you can do nothing to stop it, but it is likely to arise if employees need AI capabilities and don’t have sanctioned options. In practice, when official AI tools are blocked, people will find their own solutions. As Wade Foster puts it, if an organization can’t start there with approved tools, people are going to find a place to start somewhere. The lesson: expect shadow AI and plan to manage it, rather than deny its existence.

Can organizations fully control AI usage?

Full control is very difficult. Generative AI is so accessible that many tools are free or embedded in apps that determined users can usually work around blocks. Industry data suggests most employees will touch AI in some form. For example, one survey found over 90% of companies had workers using personal AI tools for work, even though only ~40% had bought official AI licenses. Instead of chasing an impossible goal of 100% control, security teams focus on closing critical visibility gaps and enforcing controls on high risk data. The aim is to govern AI usage where possible, knowing that some degree of informal use may persist.

Is banning AI ever justified?

Blanket bans are generally discouraged because they create more problems than they solve. In rare cases, a very tight short term ban on a specific tool might be used for example, if a new tool is discovered to leak data or violate regulations. However, even then it should be accompanied by quick efforts to provide an approved alternative. Long term broad bans like no employee may use any generative AI tend to be counterproductive. Most experts say education, policy, and controls are a better path than prohibition. If a temporary ban is necessary to complete a risk assessment, leaders should clarify the path for tool approval so that the ban is lifted as soon as safely possible.

How can we detect shadow AI use?

Detection typically relies on the same principles as shadow IT discovery. Network and web traffic monitoring can flag connections to known AI services, especially if combined with SSL inspection or AI service fingerprinting. Data Loss Prevention DLP tools can watch for sensitive content leaving the organization e.g. large text uploads to chatbots. Some companies use cloud access security brokers CASBs or proxy logs to see users logging into personal AI websites. Auditing and employee surveys may also uncover shadow AI usage. Industry guidance recommends a mix of proactive audits and continuous monitoring. For instance, F5 suggests organizations consider using ethically and legally compliant monitoring to detect unauthorized use and then respond by educating users. The goal is not heavy handed spying, but enough visibility to know where risks might be lurking.

Does providing an approved AI tool eliminate shadow AI?

Providing sanctioned tools certainly helps, but it may not eliminate shadow usage entirely. Some users might still look elsewhere if the approved tool doesn’t meet a specific need or if it’s inconvenient. Wade Foster warns that partial solutions can backfire. He observed that giving some teams access while others are blocked leads those left out to continue searching for workarounds. The key is that any approved tool must meet real needs and be easy to use. It helps to involve pilot users in selecting and shaping the tool so it actually solves their problem. Combining approved tools with supportive policy and quick feedback loops so new needs can get fast consideration is more effective than assuming one tool solves all needs.

How should we respond if we find shadow AI use?

Respond with curiosity and education, not punishment. An instance of shadow AI often means there was a legitimate business need. Security should ask what problem you were solving? and try to help address it safely. This might involve explaining risks to the user, showing them the approved alternatives, or accelerating approval of a needed tool. F5 Labs explicitly advises responding to unauthorized AI use with training, not reprimand. If a true security incident occurred e.g. data leak via a chatbot, that should be handled in the incident process. But even then, the lesson is to strengthen controls and communication rather than purely penalize the user. The more employees feel they can come forward with AI questions instead of hiding, the quicker security can manage the risk.

How do we balance security with innovation around AI?

Striking this balance is at the heart of the issue Foster raises. It involves changing from a fear of risk mindset to a managed risk for innovation mindset. In concrete terms: involve business teams early in creating AI policies, so security understands use cases, establish a small cross functional AI governance board that can make rapid decisions, and allocate a risk budget for experimentation with clear rules rather than shutting it down. Security leaders might also pilot emerging AI projects themselves to understand them or partner with departments like R&D. The goal is to ensure that as your company innovates with AI, it doesn’t do so blind. Tools like secure enclaves for AI model testing, synthetic data sets, or on premise AI runners can help provide safe spaces. Ultimately, it’s about trust: trust your people to use AI responsibly, but keep an eye on the door they’re leaving it through.

Wade Foster’s blunt message is a wake up call: forbidding AI tools outright will not stop their use, it only makes usage invisible and uncontrolled. For CISOs and security leaders, the core insight is to flip the default. Instead of erecting a wall, build a gate. Provide clear guardrails and approved tools so that AI use happens in the open and under watch. By doing so, security teams reclaim visibility and can mitigate risks in real time, rather than leaving a shadow ecosystem unchecked. Shadow AI is born from prohibition, so the better approach is pro active governance, empower safe AI usage and get ahead of the risk, because managed risk will always beat unmanaged risk.

About the Author

Mohammed Khalil is a Cybersecurity Architect at DeepStrike and the owner of CyberTrustLog. Specializing in advanced penetration testing and offensive security operations, he holds certifications including CISSP, OSCP, and OSWE. Mohammed has led numerous red team engagements for Fortune 500 companies, focusing on cloud security, application vulnerabilities, and adversary emulation. His work involves dissecting complex attack chains and developing resilient defense strategies for clients in the finance, healthcare, and technology sectors.