Security and risk leaders say they are least prepared for attacks involving autonomous botnets, adversarial manipulation and data poisoning, according to PwC’s 2026 Global Digital Trust Insights survey. The figures measure leaders’ perceived preparedness—not how often these attacks occur or how likely they are.
Which AI-related threats concern leaders most?
In PwC’s survey question about AI-enabled attacks, 53% of security and risk leaders said they were least prepared to address compromise by autonomous botnets. Adversarial attacks and data poisoning each drew 52%. The one-point difference is small; these results should be read as closely grouped preparedness concerns, not a reliable ranking of real-world danger.
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Separately, 50% of security leaders identified attacks targeting AI systems among the cyber threats they were least prepared to address. That is a distinct survey result from the three figures above, which concern named AI-enabled attack types.
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PwC surveyed 3,934 business and technology leaders in 71 countries and territories from May through July 2026, and published the findings on October 1, 2026. The multinational, cross-industry results describe respondents’ views; they do not establish that every sector, region or organization faces the same risks.
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What do the three attack types mean?
| Threat | Plain-language explanation | Where it interferes | Leaders least prepared |
|---|---|---|---|
| Compromise by autonomous botnets | The survey names concern about compromise by autonomous botnets but does not define a particular malware family or provide a technical taxonomy. | Coordinated compromise or activity at scale. | 53% of security and risk leaders in PwC’s 2026 survey. |
| Adversarial attacks | Subtle changes to what an AI system receives or perceives, intended to make it produce an incorrect answer. This is a plain-language gloss, not a complete technical definition. | The system’s inputs or perception. | 52% of security and risk leaders in PwC’s 2026 survey. |
| Data poisoning | Misleading records inserted into data used to train a model. It is not a synonym for every kind of data corruption, nor for prompt injection. | The data used during model training. | 52% of security and risk leaders in PwC’s 2026 survey. |
These categories point to different points of interference: coordinated activity, manipulated inputs, and compromised training data. PwC’s reported percentages indicate perceived readiness for the named threats, not verified attacks or incident rates. Help Net Security offers the accessible descriptions of adversarial attacks and data poisoning; PwC is the source for the survey figures.
Are companies prepared to defend AI systems?
The survey suggests investment intentions are not the same as complete readiness. While 84% of respondents expected cyber budgets to increase, only 39% said their organization had fully formalized and integrated operational continuity plans specifically addressing cyber risks. Those figures describe reported plans and preparedness at the time of the survey, not proof that larger budgets will close the gap.
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Data governance also appears incomplete. PwC reported that organizations had implemented three of seven key data-risk measures on average, and only 5% had fully implemented all seven. About half reported fully implemented data classification policies (49%) and data loss prevention across key egress channels (48%). The survey’s figures make data controls a practical area to examine alongside defenses aimed directly at AI models.
How cautious are leaders about autonomous AI defense?
Respondents showed restraint about giving AI agents authority to act in cyber defense. In PwC’s survey, 22% would authorize fully autonomous actions, 38% favored partial autonomy, and 36% preferred human-led execution with AI support.
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The barriers respondents identified help explain that caution: 55% ranked AI reliability and maturity among their top three obstacles to greater agent autonomy, 46% cited accountability and explainability, and 44% cited skills for oversight and governance identified by CISOs. These are reported barriers, not a guarantee that a particular control will make an agent safe.
For consequential actions where correctness must be assured, PwC suggests gating an agent’s output with a control the agent cannot influence—for example, a separate review, approval or validation step. Organizations can also consider specialist managed security services: respondents ranked AI (53%) and cloud security (49%) among their leading managed-security priorities for the coming year. Those survey priorities do not endorse a particular provider.
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