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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11In Verizon’s 2025 Mobile Security Index, 85% of surveyed organizations said mobile device attacks were on the rise. That is a reported organizational trend—not a count of attacks worldwide or a measured rate of successful compromise. The same survey shows why security teams are watching generative AI: respondents expect AI to make phishing, deepfake impersonation and ransomware more dangerous, while relatively few organizations say they have specific controls for these threats.
What Verizon’s 85% finding does—and does not—say
The 2025 Mobile Security Index (MSI) is Verizon’s annual report on mobile and IoT threats. Its 85% figure reflects what organizations surveyed said about the direction of mobile device attacks. Verizon’s news release also reports that 75% of surveyed organizations increased mobile-security spending over the preceding year. These are survey responses, not independently measured attack counts or proof that spending prevented incidents. Verizon’s 2025 report says respondents reported rising attacks across organizations regardless of size, industry or location.
SecurityWeek describes the survey as covering nearly 800 professionals involved in mobile-device procurement, management or security. The available description does not establish respondent selection, exact field dates, weighting or representativeness, so the survey should be read as a snapshot of organizational views—not a precise estimate for every employer. SecurityWeek’s summary provides that respondent-scale description.
How AI changes the mobile threat picture
Verizon uses “AI-assisted attack” for attacks that use AI to generate or scale phishing, SMS text phishing (smishing) or malware campaigns. AI could help attackers create convincing messages more quickly or at greater volume; the report’s percentages, however, describe what respondents fear or expect, not measured outcomes from a controlled assessment.
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| 2025 MSI finding | What it represents |
|---|---|
| 34% feared sophisticated, scaled AI-powered attacks would significantly increase their exposure. | Respondents’ concern about increased exposure—not a measured increase in incidents. |
| 38% believed ransomware would become more dangerous when powered by AI. | Respondents’ belief about potential impact—not an observed ransomware success rate. |
| 77% believed AI-assisted deepfake and SMS phishing attacks were likely to succeed. | Respondents’ expectation—not evidence that 77% of such attacks actually succeeded. |
Deepfake-enabled voice phishing adds a layer of impersonation to social engineering; smishing delivers phishing messages by SMS text. The practical concern is that an employee may trust a message or caller and disclose information or act on a request. Verizon frames this as a combination of technical threats and human mistakes: “A perfect storm is brewing as AI powered attacks grow and human mistakes persist.” The statement appears in the Verizon 2025 MSI; it is the report’s wording, not a named individual’s quotation.
Why employee behavior and AI use matter
The report identifies familiar behaviors as continuing risks: employees clicking suspicious links and entering sensitive information into unapproved generative AI tools. The latter can expose proprietary or confidential material outside an organization’s approved processes. That risk is distinct from an AI-powered attack: one concerns how an employee uses an AI service, while the other concerns an attacker using AI to create or scale malicious activity.
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Mobile security therefore involves more than securing a handset. A device can be technically managed while a person still responds to a convincing message or pastes sensitive material into an unapproved tool. Policies, technical controls and employee training address different parts of that problem.
Reported control gaps in mobile defenses
The MSI also asked organizations about protections. Relatively small shares said they had controls for several AI-related or advanced threats:
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| Protection area | Organizations reporting protections |
|---|---|
| Specific controls against AI-assisted attacks | 17% |
| Protections against deepfake-enhanced voice phishing | 12% |
| Protections against zero-day exploits | 16% |
These percentages report what organizations said they had implemented; they are not results of a technical audit of each organization. They are best used as prompts for an internal gap review, not as proof that a particular company is unprotected or that a control would stop every attack.
What MDM can contribute—and what its reported association means
Mobile device management (MDM) gives an organization a way to configure devices and enforce user and app policies. Verizon’s MSI page reports that 63% of organizations using MDM regularly audited AI-generated content, compared with 48% of organizations without MDM. That is an association reported by the survey, not evidence that MDM alone caused the difference. Verizon’s MSI overview describes MDM alongside employee training and security tools as part of organizational preparation.
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MDM is useful as a policy and configuration capability, but it is not a substitute for deciding which AI tools employees may use, what information they may enter, how suspicious messages are reported, or how the organization tests its defenses.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Practical steps for organizations
- Set device and app rules. Define which devices and applications can access company data, how devices must be configured, and what happens when they fall out of compliance. Use MDM where appropriate to apply and maintain those policies consistently.
- Govern generative AI use. Identify approved AI tools, specify what confidential or personal data must not be entered, and establish a process for reviewing AI-generated material when it could affect business decisions or communications. Audit actual use rather than relying only on written rules.
- Train employees against mobile social engineering. Cover suspicious links, SMS phishing, voice impersonation and requests for sensitive information. Give staff a clear way to verify unusual requests and report suspected attacks.
- Check defenses against the report’s named gaps. Review controls for AI-assisted phishing or malware, deepfake-enhanced voice phishing and zero-day exploits. Record what is covered, what is not, and who owns each gap.
- Pair spending with reassessment. Verizon says 75% of surveyed organizations increased mobile-security spending over the previous year, but spend alone does not establish preparedness. Reassess device coverage, policies, training and response procedures as tools and threats change.
How to read the report’s evidence
The MSI is useful for understanding what surveyed organizations report, fear and say they have prepared for. It does not quantify the number of successful mobile attacks, establish an AI-caused incident rate, or show that a given defense caused a better outcome. Verizon’s report also cites Lookout research detecting more than 4 million mobile-focused social-engineering attacks in 2024; that figure belongs to Lookout’s research and reporting period, not to a Verizon measurement.
For organizations, the strongest takeaway is not that a particular AI technique has a known success rate. It is that respondents see mobile attacks rising, expect AI to make some threats more convincing or scalable, and report limited coverage for several emerging threat categories. Those signals justify checking controls and behaviors directly inside each organization.
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