We surveyed 500+ CISOs across the Fortune 2000 to understand how they are securing their generative AI initiatives. The results are alarming: only 12% feel confident in their current controls. This report presents the full findings and actionable recommendations for enterprise leaders.
Executive Summary
Generative AI has moved from pilot projects to production at an unprecedented pace. As organizations integrate large language models into customer-facing applications, internal workflows, and decision-support systems, security teams are struggling to keep up. Our research reveals a significant gap between AI adoption and AI security maturity.
Enterprises that delay implementing robust AI security controls face regulatory, reputational, and operational risks. This report provides a comprehensive analysis of the current threat landscape and the security architectures that leading organizations are adopting to mitigate risk while accelerating adoption.
Key Findings
Our survey yielded several critical insights that every CISO and technology leader should consider when planning their AI security roadmap.
- Prompt Injection is the #1 concern, with 78% of respondents citing it as their top threat vector. Attackers are increasingly crafting inputs designed to override system instructions, extract training data, or force models to produce harmful outputs.
- Data Leakage via public LLMs is still rampant, despite corporate policies. Employees continue to paste sensitive information into consumer-facing tools, creating exposure that many organizations have not yet quantified.
- Shadow AI usage has grown 300% year-over-year. Departments are subscribing to AI tools without central visibility, making governance and compliance nearly impossible.
- Model supply chain risks are emerging as a major concern, with 62% of respondents worried about poisoned or backdoored models from third-party providers.
Implications for Security Roadmaps
These findings point to a clear need for defense-in-depth: input and output guardrails to mitigate prompt injection and leakage, visibility and governance to address shadow AI, and supply chain assurance for models and dependencies. Organizations that invest in these areas now will be better positioned to scale AI safely and meet evolving regulatory expectations.
Recommendations for Enterprise Leaders
Based on our analysis, we recommend a phased approach to securing generative AI: establish clear governance and acceptable-use policies, deploy input and output guardrails at the edge, and invest in continuous monitoring and red-teaming. Organizations that implement these measures early will be better positioned to scale AI safely and maintain stakeholder trust.
Phased Implementation
Phase one should focus on governance and visibility: define acceptable use, classify AI systems by risk, and gain visibility into shadow AI usage. Phase two should deploy technical controls: input and output guardrails at the AI orchestration layer, and integration with identity and data-loss-prevention systems. Phase three should add continuous assurance: red-teaming, monitoring, and incident response. This report details the specific security architectures that leading organizations are adopting, including zero-trust design for AI workloads, dedicated AI security gateways, and integration with existing identity and data-loss-prevention controls.