As artificial intelligence becomes deeply embedded in enterprise operations, the biggest challenge facing organizations is no longer whether to adopt AI, but whether they can explain, govern, and trust the systems they have already deployed. A recent DigiCert report cited by TechRadar found that while companies continue to invest aggressively in AI, many lack the visibility, governance, and security controls needed to manage it safely. The report found that 78% of organizations have experienced AI-related security incidents or uncovered vulnerabilities, highlighting the risks of deploying AI faster than organizations can govern it.
The research also shows that AI adoption is accelerating rapidly. Three-quarters of organizations have implemented at least four AI tools within the past six months, and more than one-third have deployed over ten. However, governance has not kept pace. Only about half of organizations have established dedicated AI security budgets and formal governance policies, while nearly half lack centralized oversight of AI systems. Many businesses are still trying to create inventories of AI tools and determine where AI is being used across the enterprise.
Another major concern is explainability and accountability. The report found that only 53% of organizations can trace AI-generated outputs back to their original models and data sources, making it difficult to audit decisions, investigate errors, or demonstrate regulatory compliance. As AI agents become more autonomous and gain access to sensitive systems, traditional identity and access management frameworks are proving insufficient. Security experts warn that "shadow AI"—unauthorized AI tools used without IT approval—is creating additional risks by introducing systems that operate outside an organization's security controls and monitoring capabilities.
The report concludes that successful enterprise AI adoption now depends on governance as much as innovation. Organizations are being urged to establish comprehensive AI inventories, strengthen security policies, implement continuous monitoring, improve explainability, and assign secure digital identities to AI agents. Rather than focusing solely on deploying more AI, businesses that invest in transparency, governance, and trust will be better positioned to scale AI responsibly while minimizing security, compliance, and operational risks.
