Learn how AI-powered attacks plus human error create a mobile security 'perfect storm' and what organizations can do to defend mobile endpoints today.

Mobile devices are now the frontline of organizational risk

Basking Ridge, NJ — Mobile devices have stopped being optional conveniences; they’re front-line endpoints that attackers treat as primary targets. The Verizon 2025 Mobile Security Index (MSI) paints a stark picture: 85% of organizations see mobile attacks rising, and 75% boosted mobile security spending last year. But there’s a twist — the rapid, often unguided adoption of generative AI (genAI) by employees has materially expanded the attack surface and introduced a new class of messy, complex risks.

Why genAI makes mobile threats different

The danger genAI brings is two-fold, and both sides are worrying.

• Low preparedness for AI-assisted attacks: Only 17% of organizations reported having specific controls to block AI-assisted attacks — a yawning gap. Attackers are using genAI to scale social engineering and refine malware; these are not the clumsy phishing emails of a decade ago.
• Widespread genAI use on mobile devices: Nearly 93% of organizations say employees use genAI on mobile for day-to-day work. Over 64% list data compromise via genAI as a top mobile risk. That’s huge — people are effectively feeding corporate context into models without clear guardrails.

Put another way: adversaries have smarter toolchains, and employees have access to those same toolchains — often with zero training. From what I’ve seen, it’s like handing a complex power tool to someone who’s skimmed the manual. Things go wrong, quickly.

Human behavior remains the weakest link

The MSI sketches a “perfect storm”: sophisticated AI-enabled threats colliding with human fallibility. One detail that stuck with me: in organizations that ran smishing tests, up to 39% saw half their staff click a malicious link. That’s not an abstract metric — that’s the exact path an attacker needs for credential theft, ransomware, or supply-chain intrusions.

Imagine this: a finance analyst gets a mobile voice-transcribed message asking to approve an invoice. It references internal project codenames and mimics the manager’s cadence. With genAI, attackers can spin up that context-rich lure in minutes. The click — and suddenly you’re juggling exfiltration, outages, regulatory headaches. I’ve watched teams spiral from a single misclick to weeks of containment work. It’s brutal.

SMBs vs. enterprises: who’s more exposed?

SMBs feel the squeeze. The MSI shows 57% of SMBs believe they lack resources to respond as effectively as larger firms, and 54% feel they have more to lose from a breach. Larger organizations generally edge ahead on a few proactive defenses:

• Employee mobile security training: 66% of enterprises vs. 56% of SMBs
• AI risk training: 50% of enterprises vs. 39% of SMBs
• Advanced multi-factor authentication: 57% of enterprises vs. 45% of SMBs

But size isn’t immunity. Across the board, 63% reported significant downtime and 50% reported data loss in the past year. Those are real, billable impacts — downtime, reputation damage, compliance costs — and they explain why mobile security should live near the top of every risk register.

How to build resilience in an AI-security world

Resilience isn’t a single product purchase. It’s a layered program that blends people, policy, and technical controls. Below are practical, prioritized steps you can start on this quarter — not someday.

• Create explicit AI usage and data-handling policies. Spell out which genAI tools are approved, what corporate data may be submitted, and what’s forbidden. Make it specific: examples, do’s and don’ts, and clear escalation paths. Vague policies don’t help when someone’s under pressure and improvising.
• Expand mobile-focused training with scenario-based exercises. Smishing and genAI-assisted phishing simulations should be frequent and role-specific. Short, tactical coaching after an exercise beats a quarterly slide deck. Real-world practice measurably lowers click rates. I’ve seen teams drop repeat-fail rates by half after three targeted simulations.
• Deploy AI-aware security controls. Invest in telemetry and detection tuned for generative patterns: content analysis that understands synthesized text, anomaly detection for unusual API or model usage on devices, and heuristics for AI-driven social engineering. Plain signatures won’t cut it.
• Enforce strong authentication and least privilege. Step-up authentication for risky actions, conditional access for mobile apps, and tight app permissions. Least privilege isn’t sexy, but it’s the single most practical limiter of damage when accounts are phished.
• Integrate network and mobile security. Unified visibility across endpoints and the corporate network catches lateral movement sooner. If your SIEM and MDM aren’t talking, you’re blind to a lot of the attack choreography.

To borrow a line from Chris Novak, VP of Global Cybersecurity Solutions at Verizon Business: mobile security is “a battle fought in the palm of every employee’s hand.” I’d add: train the hand, secure the tool, and assume attackers will weaponize AI — then build for that reality.

Short-term actions for immediate impact

• Audit which genAI apps are used on mobile and immediately block or restrict risky integrations.
• Mandate privacy-preserving settings and data controls for any approved AI apps. No free-text uploads of customer PII. No exceptions without review.
• Prioritize protection for high-risk users (finance, HR, IT admins) — step-up authentication, tighter session controls, and stricter data egress policies.
• Draft an incident playbook that specifically covers AI-generated content and model abuse: who analyses the model output, how to validate claimed provenance, and escalation timelines.

Long-term strategy: adapt, iterate, and measure

Security is a continuous program, never a checkbox. Track smishing click rates, measure incidents tied to AI-generated content, and feed those metrics to the business. Use the data to justify targeted investments: better telemetry, more frequent tabletop exercises, or vendor tools that detect AI-specific threats.

There’s nuance here. Not every org needs every shiny tool. The smart move is to iterate: pilot narrow controls, measure impact, then scale what demonstrably reduces risk. I’ve watched programs succeed where leaders prioritized a few high-impact changes and then widened scope as confidence (and budget) grew.

For benchmarking, read the Verizon 2025 Mobile Security Index (MSI) — it’s a good place to start for context and control guidance. Industry research also increasingly supports a people-first posture coupled with AI-aware tooling when facing generative threats. Learn more in our guide to AI browser risks.

Key takeaways

• Mobile + AI = elevated risk: genAI amplifies attacker capabilities while mobile devices broaden the attack surface.
• Most organizations are underprepared: only a small fraction have AI-specific controls in place.
• Human behavior matters: targeted, realistic simulations and coaching reduce risk faster than generic training.
• Unify defenses: network and mobile security must be integrated and detection needs to be AI-aware.

From what I’ve observed over multiple market cycles, organizations that combine precise policy, ongoing training, and adaptive tech gain two advantages: they reduce the number of successful attacks and shorten recovery time when incidents happen. It’s not about hoarding the latest toolset — it’s changing how people interact with AI on their phones. That cultural and operational shift often separates a near miss from a headline-making breach.

Learn more about AI-specific browser and agent risks in our piece on OpenAI’s Atlas Browser: Powerful AI, Big Convenience — and Serious Security Risks, which explores how powerful AI tooling on endpoints can introduce new attack surfaces relevant to mobile and browser-integrated agents.