Enterprises Struggle to Prepare for AI and Quantum Threats, PwC Says
A PwC survey reveals that many enterprises are unprepared for emerging AI and quantum computing threats. Only 22% of leaders are willing to deploy fully autonomous AI for cyber defense, and just 21% are implementing quantum-resistant cryptography. Key AI-related threats include attacks on AI systems such as autonomous botnets, adversarial attacks, and prompt injection, which is recognized as a significant risk. Quantum computing poses a future risk to current encryption methods, with attackers potentially harvesting encrypted data now to decrypt later. Despite available technologies to strengthen cybersecurity, adoption remains low globally, highlighting a gap between technological capability and implementation.
AI Analysis
Technical Summary
The PwC 2027 Global Digital Trust Insights report surveyed nearly 4,000 business and technology leaders worldwide, finding low adoption of fully autonomous AI defense and quantum-resistant security measures. AI-assisted attacks are increasing in speed and sophistication, with prompt injection attacks identified as a top threat to AI systems. Quantum computing threatens current encryption standards, prompting the need for quantum-resistant cryptography to prevent 'harvest now, decrypt later' attacks. The survey underscores a widespread lack of preparedness and accountability for these emerging threats, despite recognition of their importance and increasing cybersecurity budgets.
Potential Impact
Organizations face increased risk from AI-driven attacks targeting their AI systems, including autonomous botnets, adversarial attacks, and prompt injection, which can leak data or execute unauthorized commands. The advent of quantum computing threatens to render current encryption obsolete, exposing previously stolen encrypted data to future decryption. The low adoption of quantum-resistant cryptography and autonomous AI defense increases the likelihood of successful attacks and data breaches. This gap in preparedness may lead to compromised data confidentiality and integrity as attackers leverage advanced technologies faster than many organizations can defend against them.
Mitigation Recommendations
No official patch or fix applies as this is a strategic and technological preparedness issue rather than a specific software vulnerability. Organizations should prioritize implementing quantum-resistant cryptography to protect data against future quantum decryption risks. Increasing adoption of AI-based defense mechanisms, including consideration of fully autonomous AI agents for cyber defense, is recommended to keep pace with AI-accelerated attacks. Enhancing workforce skills in AI oversight and governance is critical. Clear accountability for AI and quantum security within organizations should be established. These measures align with PwC's recommendations and current best practices for emerging technology threats.
Enterprises Struggle to Prepare for AI and Quantum Threats, PwC Says
Description
A PwC survey reveals that many enterprises are unprepared for emerging AI and quantum computing threats. Only 22% of leaders are willing to deploy fully autonomous AI for cyber defense, and just 21% are implementing quantum-resistant cryptography. Key AI-related threats include attacks on AI systems such as autonomous botnets, adversarial attacks, and prompt injection, which is recognized as a significant risk. Quantum computing poses a future risk to current encryption methods, with attackers potentially harvesting encrypted data now to decrypt later. Despite available technologies to strengthen cybersecurity, adoption remains low globally, highlighting a gap between technological capability and implementation.
AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
The PwC 2027 Global Digital Trust Insights report surveyed nearly 4,000 business and technology leaders worldwide, finding low adoption of fully autonomous AI defense and quantum-resistant security measures. AI-assisted attacks are increasing in speed and sophistication, with prompt injection attacks identified as a top threat to AI systems. Quantum computing threatens current encryption standards, prompting the need for quantum-resistant cryptography to prevent 'harvest now, decrypt later' attacks. The survey underscores a widespread lack of preparedness and accountability for these emerging threats, despite recognition of their importance and increasing cybersecurity budgets.
Potential Impact
Organizations face increased risk from AI-driven attacks targeting their AI systems, including autonomous botnets, adversarial attacks, and prompt injection, which can leak data or execute unauthorized commands. The advent of quantum computing threatens to render current encryption obsolete, exposing previously stolen encrypted data to future decryption. The low adoption of quantum-resistant cryptography and autonomous AI defense increases the likelihood of successful attacks and data breaches. This gap in preparedness may lead to compromised data confidentiality and integrity as attackers leverage advanced technologies faster than many organizations can defend against them.
Defensive Guidance
No official patch or fix applies as this is a strategic and technological preparedness issue rather than a specific software vulnerability. Organizations should prioritize implementing quantum-resistant cryptography to protect data against future quantum decryption risks. Increasing adoption of AI-based defense mechanisms, including consideration of fully autonomous AI agents for cyber defense, is recommended to keep pace with AI-accelerated attacks. Enhancing workforce skills in AI oversight and governance is critical. Clear accountability for AI and quantum security within organizations should be established. These measures align with PwC's recommendations and current best practices for emerging technology threats.
Technical Details
- Classification
- {"confidence":0.3,"severitySource":"default","classifier":"rss-v2"}
- Article Source
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Threat ID: 6abe717fb45efb422045260d
Added to database: 10/01/2026, 14:43:11 UTC
Last enriched: 10/01/2026, 14:43:25 UTC
Last updated: 10/01/2026, 14:47:39 UTC
Views: 5
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