Learn How to Effectively Navigate AI, Cybersecurity
May 27, 2026
Originally Published May 8, 2025
Executive Summary
AI has moved from novelty to standard business infrastructure. According to McKinsey's 2025 State of AI report, 78% of companies now use AI in at least one business function, up from 55% three years ago. But adoption figures tell only part of the story. The same technology driving workplace efficiency is expanding the attack surface, complicating compliance obligations, and creating new categories of risk most businesses aren't prepared for. This post breaks down what AI means for your cybersecurity posture, which threats deserve attention now, and how to build an approach that treats AI as both an asset and a liability to manage.
AI Is Now a Business Baseline
AI is no longer something businesses experiment with. It's woven into the tools employees use every day: email platforms, customer relationship systems, scheduling software, analytics dashboards, and communication tools. What changed isn't access to AI. It's the depth of integration.
McKinsey's 2025 State of AI report found that 78% of companies now use AI in at least one function, and 92% plan to increase their AI investments over the next three years. Generative AI alone has more than doubled its adoption rate since 2023. At the same time, Pew Research Center's October 2025 survey found only 21% of U.S. workers say they use AI tools at work on a regular basis. The gap between enterprise adoption claims and actual daily use reveals something worth paying attention to: most businesses are absorbing AI risk before they've absorbed the operational benefits.
AI involves developing computer systems capable of performing tasks that typically require human intelligence: recognizing speech, learning from patterns, planning sequences of action, solving problems, and making decisions. The practical applications range from voice assistants on smartphones to predictive analytics platforms that flag fraud before a transaction clears. That range is also what makes AI a cybersecurity challenge. Every new deployment point is a potential entry point. For a plain-language breakdown of the technology itself, see our post on the difference between AI and machine learning.
What AI Brings to Your Business
The business case for AI is real. When implemented with appropriate controls, AI-powered tools offer advantages that compound over time.
Efficiency gains come from removing repetitive manual work. Tasks that once required dedicated staff time, from scheduling and data entry to generating first-draft communications, are handled faster and with fewer errors. Decision quality improves when AI-driven analytics surface patterns in customer behavior, operational performance, or market conditions that human review would likely miss. Cost reductions follow from process automation: routine customer inquiries, invoice matching, compliance documentation, and security event triage can all be partially or fully automated, redirecting staff to higher-value work.
The benefits are genuine. So is the exposure that comes with them. If your business is still evaluating where to start, the AI FAQs for Business covers the foundational questions most leadership teams are working through right now.
The Threat Side of the Equation
The same AI capabilities making operations more efficient are also making attackers more effective. That's not a theoretical concern. It's happening now, and the targets include businesses of every size.
AI-powered phishing has eliminated the telltale signs employees were trained to catch: awkward phrasing, generic greetings, obvious translation errors. Today's AI-generated phishing emails match the writing style, vocabulary, and tone of the person being impersonated with accuracy that defeats most training programs. Attackers can generate hundreds of tailored messages in the time it once took to craft one.
Deepfakes have moved from novelty to operational tool. In one widely cited case, a finance employee at a multinational firm was deceived into wiring about $25 million after a video call in which every face and voice, including the apparent CFO, was AI-generated. The employee followed a normal internal authorization process. The money was gone before the deception was detected. Voice cloning tools can now replicate an executive's speech patterns from a few seconds of publicly available audio. We cover detection and defense controls in detail in our deepfake detection guide.
Threat snapshot: Deepfake fraud incidents now occur every five minutes globally, with average losses exceeding $500,000 per incident, according to Keepnet's 2025 statistics report. Standard cyber insurance policies were not written to cover AI-generated impersonation, and coverage disputes are increasing as claims arrive. See our post on cyber liability insurance for what to review in your current policy.
Ransomware operators are using AI to profile targets before launching attacks, analyzing data about a company's financials, insurance coverage, and operational dependencies to calibrate demands for maximum payment probability. The manual reconnaissance that once took days now takes minutes. Our post on robot hackers goes deeper on how automated attack tools are changing the threat landscape for small and midsize businesses.
Shadow AI adds an internal dimension to this risk. When employees use consumer-grade AI tools for work tasks without IT awareness, sensitive data can leave the environment entirely. Inputs to third-party AI platforms may be used to train models, stored on external servers, or exposed in ways your existing data handling policies never anticipated. Our post on Shadow AI covers how to identify and govern unauthorized AI use inside your business.
Adoption Gap: Where Most Businesses Stand
The numbers above tell a story that rarely makes headlines. Businesses are deploying AI faster than they're governing it. McKinsey found that only 5.5% of companies are seeing measurable financial returns from their AI investments, and just 1% of business leaders consider their company mature in AI deployment. That means the vast majority of businesses are carrying AI-related risk with very little of the upside locked in yet.
Artificial Intelligence Readiness Evaluation (AIRE)
STACK Cybersecurity developed a custom evaluation tool for businesses of all sizes to gauge their AI readiness. Our comprehensive assessment offers a custom score across governance, security, compliance, and implementation planning.
AI and Compliance: A Gap Most Businesses Are Ignoring
Regulatory frameworks haven't kept pace with AI deployment. CMMC 2.0, finalized without a single direct reference to AI, is a clear example. But the absence of AI-specific language doesn't mean AI usage falls outside compliance scope. It means existing controls apply the moment AI touches regulated data, and most businesses haven't mapped that exposure.
If your company handles Controlled Unclassified Information and employees are using AI tools to draft, summarize, or process that data, you have a compliance issue regardless of whether your assessor has asked about it yet. Phase 2 CMMC assessments are underway in 2026. Assessors are asking. Our post on CMMC compliance and RPO status covers the regulatory landscape in detail.
The same principle applies to SEC disclosure requirements, FTC data security obligations, and state-level AI governance laws moving through legislatures across the country. Our state AI laws guide tracks current and pending legislation. Businesses that treat AI governance as a future problem are accumulating present-day liability.
Building an AI Security Posture
Securing AI use isn't a separate program. It's an extension of the governance, risk, and compliance work you're already doing, or should be. The businesses pulling ahead aren't buying new tools. They're applying existing controls to new deployment patterns and documenting it.
Start with an inventory. Most businesses don't have an accurate picture of which AI tools are in active use across the company. You can't govern what you haven't catalogued. Map data flows next: understand what data AI tools are processing, where it goes, and who has access to the outputs. This is especially critical for businesses handling sensitive customer information, financial records, or government-controlled data.
Define acceptable use. Clear policies on what AI tools are approved, what data they can process, and what human review is required before AI outputs are acted upon give your team guardrails and give you documentation that matters during audits and insurance underwriting reviews. The AI Security Checklist is a practical starting point for building that policy framework.
Update your verification protocols. For financial transactions, executive communications requesting urgent action, and vendor interactions, AI-powered impersonation means out-of-band verification steps that weren't necessary before are now controls. A callback to a known number isn't optional. It's a requirement. Pair that with MFA across all business systems and you've addressed two of the most common entry points attackers are exploiting right now.
Review your cyber liability insurance. Many policies were written before AI-driven fraud was a practical threat. Social engineering sublimits, funds transfer fraud language, and crime coverage exclusions may leave your business exposed in ways your broker hasn't flagged. Ask specifically how your policy responds to a deepfake-assisted wire transfer.
See how a Michigan manufacturer improved their security posture from a C to an A/B rating through focused, practical controls in our Taylor Turning case study. The same fundamentals that secured their environment apply directly to AI governance.
Path Forward
Businesses succeeding with AI have matched their adoption pace to their governance capacity. McKinsey found that AI high performers are three times more likely to have fundamentally redesigned their workflows as part of AI deployment, with strong senior leadership driving the effort.
AI isn't slowing down. The threat actors using it against your business aren't either. The STACK AI Hub brings together our full library of AI resources, assessments, and tools in one place.
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AI Business Tips E-Book
When implemented incorrectly, any technology with access to sensitive data can create security risks. This e-book covers real examples, implementation guidance, and security considerations for businesses adopting AI.
Frequently Asked Questions
What is the biggest AI-related cybersecurity threat for businesses in 2026?
AI-powered social engineering, including voice cloning and deepfake video impersonation, is the fastest-growing threat category. Attackers use these tools to impersonate executives and authorize fraudulent financial transactions. Traditional verification controls and employee training weren't designed for impersonation at this level of realism.
Does using AI tools affect our CMMC compliance?
Yes. CMMC 2.0 doesn't mention AI by name, but existing controls apply the moment AI tools process or have access to Controlled Unclassified Information. If employees are using AI to draft, summarize, or review CUI, you need to assess that usage against your NIST SP 800-171 controls and document your findings before an assessor asks.
Does cyber insurance cover deepfake fraud?
It depends on how your policy is written. Standard policies don't include a dedicated deepfake coverage line. Losses are evaluated under social engineering and funds transfer fraud language, which was written for human impersonation. AI-generated deepfakes create gray areas that are increasingly landing in coverage disputes. Ask your broker specifically how a deepfake-assisted wire transfer would be handled under your current policy.
What is Shadow AI and why does it matter?
Shadow AI refers to employees using AI tools, often consumer-grade platforms like ChatGPT or image generators, without IT approval or awareness. When sensitive business data is entered into these tools, it may be stored externally, used to train third-party models, or exposed in ways your data handling policies never anticipated. A formal AI inventory and acceptable use policy are the first steps to addressing it.
How do we know which AI tools employees are actually using?
Most businesses don't have an accurate picture without a formal discovery effort. DNS filtering logs, endpoint monitoring, and browser extension audits can surface unauthorized AI tool usage. A cybersecurity risk assessment that includes an AI tool inventory and data flow mapping is the most reliable method.
Is AI use something we need to disclose to customers or regulators?
Increasingly, yes. Several states have enacted or are advancing AI transparency requirements. If your business operates in regulated industries like healthcare or financial services, sector-specific guidance from the FTC and HHS addresses AI use in customer-facing and data-processing contexts. Legal counsel familiar with your industry and operating states should be part of that conversation.
Need Help with AI Security?
STACK Cybersecurity provides AI readiness assessments, security implementation guidance, and ongoing monitoring. Check out the STACK AI Hub or reach out directly.
Email: info@stackcyber.com
Phone: (734) 744-5300