Enterprise AI enters risk management phase as security concerns rise, CompTIA research finds

Companies are increasingly discovering that the challenge with AI is no longer adoption, but execution

As artificial intelligence (AI) expands into core business operations, attention is shifting from adoption to execution risk, according to research and analysis from CompTIA.

Governance, cybersecurity and workforce readiness are emerging as critical factors in determining whether AI initiatives deliver long-term business value, CompTIA’s new research brief “Practical Implications of AI Safety” finds.

“AI safety, governance and workforce development are becoming competitive advantages rather than compliance exercises,”

“Enterprise AI has entered a new stage of maturity,” said Seth Robinson, vice president, research, CompTIA. ” Effective governance structures, strong cybersecurity practices and workforce readiness are becoming central requirements for successful deployment and long-term success.”

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About six in 10 organizations are shifting from experimentation and individual productivity use cases toward enterprise-wide implementation, according to CompTIA’s “Corporate AI Adoption” report. The same survey found that cybersecurity concerns rank as the second-largest challenge to AI adoption, behind only finding the right balance between human employees and AI-powered tools and agents.

The need for security-related elements within organizational AI policies is widely acknowledged. Among companies surveyed by CompTIA, 98% cited the importance of risk management; 96%, data protection and privacy; and 95%, governance. On the investment front, 48% of organizations say cybersecurity will be a key focus of their AI spending – the same percentage as planned investment in employee AI training.

CompTIA has identified three priorities as businesses navigate the next phase of AI adoption.

1. Align AI strategy with existing governance frameworks. Organizations should build AI policies and decision-making processes on the foundation of existing governance, compliance and risk-management practices.
   
   
2. Build resilient decision-making structures. Cross-functional collaboration among IT, security, legal, compliance and business stakeholders can help organizations adapt as AI technologies, regulations and best practices evolve.
   
   
3. Invest in workforce readiness. Successful AI implementation depends on both technical and organizational capabilities. Companies must address AI and cybersecurity skill gaps while strengthening foundational technology competencies.

“AI safety, governance and workforce development are becoming competitive advantages rather than compliance exercises,” Robinson said. “Organizations that approach AI with the same discipline they apply to cybersecurity and digital transformation will be better positioned to capture business value while managing emerging risks.”