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In PwC’s 2026 Global Digital Trust Insights survey, security and risk leaders said they were least prepared to address attacks involving autonomous botnet compromise (53%), adversarial attacks (52%) and data poisoning (52%). These are responses about perceived preparedness—not evidence that the threats occur most often or are most likely.
What did leaders say they were least prepared for?
In a separate measure, 50% of surveyed security leaders named attacks targeting AI systems among the cyber threats they were least prepared to address. The three named AI-enabled attack types—autonomous botnet compromise, adversarial attacks and data poisoning—were each reported in a question about preparedness for specific attacks. The figures are close, and they do not establish a meaningful difference in real-world danger.
PwC surveyed 3,934 business and technology leaders across 71 countries and territories from May through July 2026, publishing the findings on October 1, 2026. The multinational, cross-industry results describe respondents’ views; they should not be assumed to represent every organization, industry or region equally. PwC’s 2026 Global Digital Trust Insights provides the survey context and results.
What are the three AI-related threats?
| Threat | What the term means here | Leaders reporting they were least prepared |
|---|---|---|
| Compromise by autonomous botnets | Concern about compromise by autonomous botnets. PwC does not provide a technical taxonomy or identify a particular malware family in this finding. | 53% of security and risk leaders |
| Adversarial attacks | Attacks that subtly alter what an AI system sees so it produces an incorrect answer. This is a plain-language description, not a complete technical definition. | 52% of security and risk leaders |
| Data poisoning | Misleading records inserted into data used to train a model. This is not a synonym for every kind of data corruption or for prompt injection. | 52% of security and risk leaders |
The percentages are PwC survey responses about perceived preparedness for these attack types. They are not attack-rate measurements. Help Net Security’s report on the findings offers the accessible descriptions of adversarial attacks and data poisoning.
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Are companies prepared to defend AI systems?
The survey points to investment alongside practical readiness gaps. While 84% of respondents expected cyber budgets to increase, just 39% said their organization had fully formalized and integrated operational continuity plans specifically addressing cyber risks. On data risk, companies had implemented an average of three of seven key measures; only 5% had fully implemented all seven. Full implementation of data classification policies was reported by 49%, and data loss prevention across key egress channels by 48%.
Those measures matter because organizations need to know what data they hold, protect sensitive information as it moves, and plan how operations continue when cyber incidents disrupt systems. The survey figures do not show that any single control prevents the named AI threats, but they illustrate why preparedness extends beyond buying AI-specific tools. See PwC’s readiness findings.
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How much autonomy are leaders willing to give AI cyber defenses?
Leaders were cautious about allowing AI agents to act without human oversight. In PwC’s survey, 22% would authorize fully autonomous actions for cyber defense, 38% favored partial autonomy, and 36% preferred human-led execution with AI support. These are distinct levels of authority, not measures of how effective the approaches are.
The reported barriers included AI reliability and maturity (55%), accountability and explainability (46%), and skills for oversight and governance identified by CISOs (44%). Where a response must be correct before it has consequences, PwC recommends gating an agent’s output with a review, approval or validation control the agent cannot influence. That keeps responsibility and a check on consequential actions outside the system being checked. More detail is available in PwC’s findings on AI agents in cybersecurity.
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What can organizations take from the findings?
- Separate preparedness from likelihood. Treat the survey as a signal of where respondents feel least equipped, not a ranked forecast of which attack will happen next.
- Review foundational data and continuity controls. Assess data classification, loss prevention and cyber-specific continuity planning alongside AI-focused safeguards.
- Set authority boundaries for AI agents. Define which actions can be automated, which require approval, and how outcomes are validated when mistakes could have serious consequences.
- Consider specialist support where needed. PwC reports that security leaders ranked AI and cloud security among their top priorities for managed security services over the coming year. This is a category-level finding, not an endorsement of a particular provider. PwC’s managed security services findings give that context.
As Matt Rowe, Chief Security Officer at Lloyds Banking Group, put it: “To be successful in the era of AI, we need to be able to defend at machine speed.” PwC’s findings page also attributes this observation to Phil Venables, Partner at Ballistic Ventures: “AI is creating both a new class of security risk and one of the biggest opportunities defenders have had in decades. The challenge is building trust and control at the same speed as adoption.”
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