Key Takeaways
- Only about half of cybersecurity professionals trust autonomous AI for narrowly defined tasks, and far fewer rely on it for broader actions like patching or alert dismissal.
- Data privacy worries, lack of human intuition, accountability concerns, and uncertain outcomes are the primary barriers to faster agentic AI adoption in security.
- Despite trust gaps, AI is viewed as essential: 94 % of organizations use large language models, and most believe AI will improve threat detection and surpass human capability in spotting elusive threats.
- AI is also seen as a growing risk; more than a third of security leaders rank it as their top cybersecurity threat, overtaking ransomware and malware for the second year.
- Finance leaders plan to increase AI spending, but the majority demand demonstrable ROI before allocating additional funds to finance‑technology initiatives.
- About two‑thirds of finance teams already apply AI to specific accounts‑payable processes, yet scaling remains a challenge, and compliance is prioritized over raw innovation.
- Supply chain and workforce risk has risen for 77 % of companies, with many experiencing operational delays due to limited visibility into third‑party risks.
- Growing complexity of compliance requirements is cited as a leading barrier to effective risk response, while AI adoption in supply‑chain risk management is widespread but not yet fully embedded.
Overview of AI Trust in Security Operations
A recent global survey of 1,350 security and IT decision‑makers conducted by Arctic Wolf reveals that only 53 % of respondents trust autonomous AI to perform narrowly defined security actions, such as blocking malicious IP addresses or domains. Confidence drops sharply for broader tasks: just over 40 % trust AI to automatically patch known vulnerabilities, and fewer than 30 % believe AI can reliably dismiss alerts deemed non‑issues. This limited trust underscores a cautious stance among security leaders toward granting agentic AI greater control over operational workflows.
Barriers to Agentic AI Adoption
When asked why they hesitate to expand AI’s role, roughly 50 % of survey participants cited data‑privacy concerns and the perceived lack of human intuition as major impediments. Additional factors included worries about accountability for AI‑driven decisions and the risk of inaccurate outcomes that could exacerbate security incidents. Consequently, only 14 % of organizations have made AI a central pillar of their security‑operations strategy, indicating that most firms remain in an exploratory or limited‑use phase.
AI’s Role in Modern Security Operations
Despite these reservations, the survey shows that AI is nevertheless considered a prerequisite for contemporary security defenses. A striking 94 % of organizations report using large language models (LLMs) in some capacity, and 51 % view AI functionality as a non‑negotiable requirement when evaluating technology vendors. Moreover, 85 % anticipate that AI will enhance their ability to detect new or evasive threats, while 72 % believe AI outperforms humans in threat identification, highlighting a strong belief in AI’s analytical advantages even as trust in autonomy lags.
AI as a Double‑Edged Sword: Emerging Threats
The same research flags AI as a growing source of risk. More than a third (35 %) of security and IT leaders identified AI as their single biggest cybersecurity threat, surpassing ransomware and malware for the second consecutive year. This paradox—relying on AI for defense while simultaneously viewing it as a potent offensive tool for adversaries—reflects the dual nature of the technology and underscores the need for robust governance, continuous monitoring, and threat‑intelligence sharing to mitigate AI‑enabled attacks.
Finance Leaders’ AI Investment Expectations
Shifting focus to the finance sector, a Basware‑Forrester survey of 231 enterprise finance leaders across the US, UK, France, and Germany found that 76 % plan to increase AI investment over the next two years. However, 68 % insisted they must see a clear return on investment before committing additional funds to finance‑technology projects. This ROI‑first mindset suggests that while enthusiasm for AI is high, financial executives demand tangible efficiency gains, cost savings, or revenue enhancements to justify further spending.
Current AI Use in Finance and Compliance Priorities
The survey also revealed that about two‑thirds (67 %) of finance teams already deploy AI for specific accounts‑payable functions, such as invoice matching or exception handling. Yet scaling these pilots remains a hurdle. When it comes to strategic priorities, 64 % of respondents said they favor stability and compliance over raw innovation when selecting AI solutions. Nearly half (46 %) feel they have achieved an effective balance between governance and innovation, but 65 % acknowledged that major or urgent improvements are needed to keep pace with evolving financial regulations.
Supply Chain and Workforce Risk Trends
Turning to supply‑chain security, an Avetta survey of 500 senior supply‑chain and safety managers at large US companies showed that 77 % experienced a rise in global supply‑chain and workforce risk over the past 12 months. More than two‑thirds (68 %) reported workforce‑related operational delays or shutdowns during the same period, indicating that human‑factor disruptions are now a material concern alongside traditional logistics challenges.
Challenges in Supplier Visibility and Compliance Complexity
A key contributor to these delays is limited insight into third‑party risk: 38 % of respondents identified insufficient visibility into supplier‑side risks as a major barrier to effective vulnerability response. Simultaneously, almost three‑quarters (71 %) said compliance requirements have grown more complex over the past year, with 42 % labeling this increased complexity as a leading obstacle to timely risk mitigation. The intersection of opaque supply chains and tightening regulatory expectations is creating a perfect storm for operational vulnerability.
AI Adoption in Supply Chain Risk Management
On the technology front, the Avetta survey found that virtually every organization surveyed (97 %) is employing AI in some aspect of supply‑chain risk management, reflecting strong recognition of its potential. However, widescale deployment remains incomplete: only 38 % reported that AI is fully embedded across their operations, while the majority are still in pilot or partial‑implementation stages. This gap suggests that while interest in AI‑driven risk analytics is high, organizations face integration, data‑quality, and change‑management challenges that impede full realization of AI’s benefits.
Conclusion and Implications
Across security, finance, and supply‑chain domains, the data paint a consistent picture: organizations are eager to harness AI’s analytical power but remain cautious about granting it autonomous authority. Trust hinges on demonstrable ROI, robust governance, and clear accountability mechanisms. Privacy concerns, the need for human intuition, and regulatory complexity act as friction points that slow broader adoption. To move beyond experimentation, leaders must invest in transparent AI models, strengthen data‑privacy safeguards, and develop frameworks that balance innovation with compliance. Only by addressing these barriers can firms unlock AI’s full potential to improve threat detection, financial efficiency, and supply‑chain resilience while managing the emergent risks that the technology itself introduces.