AI Hallucinations, Data Leaks, and Deepfakes:The Executive's Guide to AI Risk in 2026

Ask most executives when they plan to address AI risk, and the answer usually lands somewhere in the future. A policy is coming. A governance framework is being discussed. Someone is looking into it.

That answer made sense two years ago. It doesn't anymore.

AI is already embedded in your operations. Your finance team is using it to draft reports. Your HR department is using it to summarize applications. Your legal team is using it to pull together contract summaries. And that's just the tools you know about.

Meanwhile, cybercriminals are using the same technology to clone executive voices, impersonate CFOs in video calls, and craft phishing emails that read exactly like they came from your CEO.

Fraud losses from generative AI are expected to climb from USD 12.3 billion in 2024 to USD 40 billion by 2027. The question is no longer whether your organization faces AI-related risk. The question is which category hits you first.

There are three distinct threats every executive needs to understand: AI hallucinations, data leaks from shadow AI, and deepfake fraud. They look different on the surface, but they share one root cause: AI adoption has outpaced the governance around it. Here's what each one means and what to do about it.

AI Hallucinations: When Confident Is Not the Same as Correct

What It Is, in Plain English

AI doesn't know what it doesn't know. It generates answers with full confidence even when it's making things up. That's the core of the hallucination problem.

It isn't a glitch. It isn't a sign that a particular model is broken. Hallucination is a structural feature of how large language models work. They predict the most statistically plausible next word, not the most factually accurate one. The result is an AI that sounds authoritative even when it's wrong.

Why It Matters for Your Business

The risk isn't just incorrect outputs in isolation. It's incorrect outputs embedded in decisions, presented in reports, passed on to clients, or used as the basis for legal or financial action.

Even the best-performing AI models produce hallucinations on at least 7 out of every 1,000 prompts during basic summarization. That rate climbs sharply in specialized domains: hallucination rates in legal contexts have been measured at 18.7%, and in medical contexts at 15.6% or higher. On difficult knowledge questions, the majority of tested AI models are more likely to hallucinate than give a correct answer.

For a business, the exposure surfaces are everywhere: legal summaries, financial analysis, compliance documentation, customer-facing content, research memos. Anywhere an AI output is trusted without a human review layer is a potential failure point.

Gartner has flagged AI hallucination as a direct threat to both decision-making and brand reputation. The 2026 International AI Safety Report, produced by more than 100 experts from over 30 countries and backed by the OECD, the EU, and the United Nations, reached a similar conclusion: the most pressing AI risks come not from the models themselves, but from the complex systems organizations build around them without adequate oversight.

From an enterprise risk perspective, hallucination is not just a technical failure. It is a governance failure: producing fabricated outputs when internal uncertainty exceeds an acceptable threshold.

The Questions to Ask Yourself

• Where in our organization are AI outputs being acted on without human verification?
• Do we have a review layer for AI-generated content in legal, financial, or compliance contexts?
• Do employees know that AI can be confidently wrong, and do they know how to flag it?

Data Leaks: The Breach No One Sees Coming

The Shadow AI Problem

When most people think about data breaches, they imagine an outside attacker breaking through a firewall. The growing reality is different. The breach is often an employee sitting at their desk, pasting a confidential document into ChatGPT to get a faster summary.

They aren't trying to cause a breach. They're trying to finish their work before lunch.

This is what's known as shadow AI: the use of unauthorized AI tools within an organization, without IT oversight, without governance, and without any visibility into where the data goes.

Nearly half of all generative AI users access these tools through personal accounts that their employers have no oversight of. The average organization now experiences 223 incidents per month of employees sending sensitive data to AI apps. And in a study spanning 1,000 enterprise environments, 99% had sensitive data exposed to AI tools due to insufficient access controls.

The scale of the problem is striking: 77% of employees who use AI tools have pasted company information into them, and 82% of those did so through personal accounts outside any enterprise security controls.

What Is Actually Leaving the Building

This isn't an abstract security concern. The data walking out the door through consumer AI tools is specific and consequential:

• Financial projections and M&A materials
• Legal documents and pending litigation details
• Customer records and personally identifiable information
• HR files and employee performance data
• Proprietary source code and product plans

A well-publicized example: engineers at Samsung leaked proprietary source code, internal meeting transcripts, and semiconductor test data through ChatGPT within the span of a single month. None of them intended to expose company information. Each was simply using a productivity tool to move faster.

The dynamic is worth naming plainly: traditional shadow IT required someone who understood they were going around the rules. Shadow AI just needs someone with a browser trying to get their work done before end of day.

The Compliance Dimension -- Especially in Canada

For Canadian organizations, this is where shadow AI becomes more than an internal security concern. It becomes a regulatory liability.

Canada is currently operating under PIPEDA, a federal privacy law written in 2000, with no dedicated federal AI framework in place. Bill C-27, which would have introduced the Consumer Privacy Protection Act and Canada's first AI-specific legislation under the Artificial Intelligence and Data Act, died on the order paper in January 2025 when Parliament was prorogued. As of early 2026, new federal privacy legislation is anticipated but has not yet been introduced.

In the absence of a modern federal standard, Quebec's Law 25 has become the most stringent active framework in the country. Organizations collecting personal information from Quebec residents face obligations around consent, privacy impact assessments, and breach reporting that apply directly to how AI tools handle sensitive data. Many organizations are also aligning with GDPR as a practical baseline for international data transfers.

The compliance gap matters because shadow AI doesn't just create security exposure. When an employee uploads protected health information, client financial data, or employee records into an unvetted third-party AI platform, it can constitute a regulatory violation without a single malicious actor involved. GDPR fines for AI-related violations are expected to begin materializing in late 2026 and early 2027.

Shadow AI breaches cost an average of $670,000 more than traditional security incidents, according to 2025 data. That number doesn't include the regulatory penalties or reputational consequences that follow.

The Questions to Ask Yourself

• Do we have an AI acceptable use policy, and does every employee know it exists?
• Are we providing sanctioned, enterprise-grade AI tools -- or leaving staff to find their own?
• Do we have visibility into which AI applications are being used across the organization?

Deepfakes: The Threat That Is Already Costing Companies Millions

What It Is, in Plain English

Deepfake technology uses AI to convincingly replicate human voices, faces, and video in real time. In the wrong hands, it can make a fraudster sound exactly like your CFO, look exactly like your CEO on a video call, or impersonate a trusted colleague with enough accuracy to pass a human review.

The most frequently cited example remains instructive: in early 2024, a finance employee at the global engineering firm Arup was deceived into transferring USD 25 million after participating in a video call where every other participant, including the apparent CFO, was a deepfake. There was no phishing email. No malware. Just a convincing fabricated meeting.

That incident is no longer an outlier.

The Numbers Are Moving Quickly

Financial losses from deepfake-enabled fraud exceeded $200 million in the first quarter of 2025 alone. AI-powered deepfakes were involved in more than 30% of high-impact corporate impersonation attacks in 2025. Deepfake-enabled CEO fraud now targets an estimated 400 companies per day.

Deepfake files have grown from 500,000 in 2023 to a projected 8 million in 2025. Fraud losses tied to generative AI are expected to reach $40 billion in the United States by 2027, growing at a compound annual rate of 32%.

What makes this particularly concerning for executives: only 13% of companies have any anti-deepfake protocols in place, and roughly one in four executives reports limited or no familiarity with the technology.

The Attack Vectors to Know

Understanding how deepfake fraud actually works is the first line of defense. The most active attack vectors right now include:

• Voice cloning in phone and audio calls: a 30-second audio sample is enough to clone an executive's voice with high accuracy.
• Fabricated video calls: attackers synthesize video of known executives or colleagues to conduct fake meetings and authorize fraudulent transactions.
• Deepfake job candidates: the FBI and the Department of Justice have both issued warnings about foreign operatives and bad actors using deepfake technology to pass interviews and gain access to internal systems post-hire. Experian's 2026 fraud forecast identifies this as the second-greatest fraud threat of the year.
• AI-generated phishing content: 82.6% of phishing emails are now created using AI, a 53.5% increase year over year.

The Canadian regulatory angle is developing here too. Federal privacy reform under consideration explicitly identifies deepfakes as a priority area. Organizations that have not prepared policies and procedures for deepfake-related fraud will face both operational exposure and increasing regulatory scrutiny as that framework takes shape.

The Questions to Ask Yourself

• Do we have an out-of-band verification protocol for large financial transfers -- a callback process that doesn't rely solely on what someone sees or hears on a call?
• Have we briefed our finance and HR teams on the realities of deepfake fraud?
• Do we have a process for verifying the identity of job candidates before granting system access?

The Common Thread: Governance Has Not Kept Up With Adoption

Three different threats. Three different attack surfaces. One shared problem.

AI adoption inside most organizations has moved faster than the governance around it. Only 37% of organizations have AI governance policies in place. That means 63% are operating without guardrails.

This is the central finding of the 2026 International AI Safety Report: the problem is not the models. It is the complex systems organizations have built around them, without the oversight, accountability structures, and human review layers that responsible deployment requires.

AI is no longer a pilot program or an experiment. For most organizations, it is now operational infrastructure. It is being used to make decisions, draft communications, process data, and interact with clients. Infrastructure at that scale needs to be governed like infrastructure.

The organizations that will come out ahead are the ones treating AI governance as a leadership priority today, not a compliance project to be addressed after the first incident.

What Smart Executives Are Doing Right Now

You don't need to become a technical expert to lead on this. What you do need is a clear organizational posture and a few concrete actions.

1. Map where AI lives in your organization -- sanctioned and unsanctioned.
   You cannot govern what you cannot see. Start with an honest inventory of every AI tool in use across every department, including the ones IT didn't approve. Security teams consistently report that the number of unauthorized AI applications employees are using far exceeds what leadership assumes.

2. Establish a human review layer for high-stakes AI outputs.
   Legal documents. Financial analysis. Compliance filings. External client communications. Any context where an AI error carries significant consequences needs a human sign-off before action is taken. Make this explicit policy, not an informal expectation.

3. Create -- or update -- an AI acceptable use policy.
   If your organization doesn't have one, create it. If you do have one, check when it was last updated and whether employees have actually seen it. A policy that lives in a shared drive and has never been communicated is not a policy.

4. Implement out-of-band verification for large financial transactions.
   This is the single most practical defense against deepfake fraud. A simple callback protocol -- a separate, pre-established verification step that does not rely on what someone sees or hears in a call -- could have prevented the $25 million Arup transfer. It takes minutes to design and costs nothing to implement.

5. Brief your leadership team on the basics.
   Awareness is the first line of defense against deepfakes and social engineering. Your executive team doesn't need a technical briefing. They need to understand that voice cloning is real, that video calls can be fabricated, and that verification protocols exist for exactly that reason.

The Time for a Plan Is Before the Incident

The organizations making the news for AI-related breaches and fraud losses are rarely the ones that lacked technical sophistication. More often, they are the ones that simply hadn't thought through the basics before the moment arrived.

The good news: the fundamentals of AI risk management are not complicated. A clear acceptable use policy. A governance framework. A human review layer on critical outputs. An out-of-band verification protocol for transactions. These are not expensive or technically demanding. They are leadership decisions.

The Canadian regulatory environment is actively evolving. New federal privacy legislation is expected to introduce significant obligations around AI and data governance, with penalties that rival those seen in Europe. The organizations best positioned for what's coming are the ones building their governance practices now, before a regulator or a breach forces the issue.

Not sure where your organization's AI risk exposure starts? That's exactly the conversation we're built for. Connect with The Driz Group to talk through where you stand and what a practical governance approach looks like for your business.