Preparedness for Real-time AI Demands Questioned by Survey: Answer Is Negative

In a recent survey conducted by the Ponemon Institute in partnership with OpenText, 1896 IT leaders shared their insights on the challenges and priorities in achieving AI readiness. The survey highlighted the transformative potential of AI, but also revealed a significant gap in information readiness that many organizations are struggling to bridge.

One of the key findings of the survey is that almost three-quarters (73%) of CIOs, CISOs, and other IT leaders believe reducing information complexity is key to AI readiness. As organizations increasingly rely on AI and GenAI, the ability to observe, monitor, and intervene in AI-driven processes in real time emerges as the most pronounced challenge for enterprises of all sizes in the months and years ahead.

To address this challenge, organizations are implementing continuous real-time AI monitoring systems that track data streams and identify anomalies, enabling immediate human intervention when necessary. Additionally, they use explainable AI tools for transparency and compliance, establish clear governance policies, conduct regular training on ethical AI use, and create independent ethics committees to oversee AI deployment and ensure responsible operation.

However, the survey also shows that ensuring the high availability of IT services is a top priority of IT leaders to support business innovation sought through AI. Almost half (47%) of respondents point to this as the best path to innovation.

Another significant challenge identified by the survey is the security of AI systems. Forty-four percent of respondents mention the challenge of securing source code, and an equal number (44%) cite the challenge of securing custom data. The survey's authors suggest that organizations should develop an AI data security program, use tools to validate AI prompts and their responses, train teams to spot AI-generated behavior patterns or threat actors, use data cleansing and governance, and identify and mitigate bias in AI models for safe and responsible use.

The survey also indicates a challenge for organizations in managing real-time observability and intervention in their AI systems. Fifty-three percent of respondents find it very difficult to support business goals and transformation, and forty-nine percent of respondents say their organizations are not effective in their ability to monitor insider activity across hybrid and or remote environments.

The top use case for genAI is to support security operations such as analyzing alerts and generating playbooks, cited by 39% of respondents. Thirty-four percent of respondents also look to genAI to accelerate threat detection or incident response. Real-time responsiveness is a leading use case for genAI, according to the survey.

Ensuring trust is another important aspect of AI readiness. Fifty-nine percent of respondents report their organizations are not effective in creating trust while taking steps to reduce data loss incidents caused by negligent or malicious insiders. Preventing the exposure of sensitive information, strengthening encryption practices, and reducing the risk of poor or misconfigured systems due to over-reliance on AI for cyber risk management are key to achieving AI readiness.

Recruiting and retaining qualified personnel is another top priority for IT leaders, with 43% saying it is the best path to innovation. The survey does not provide information on the geographic location or industry of the organizations involved.

In conclusion, the Ponemon-OpenText survey reveals a complex landscape for organizations as they strive to achieve AI readiness. While executives recognize the transformative potential of AI, there is a gap in information readiness causing organizations to struggle in securing, governing, and aligning AI initiatives across business. To address these challenges, organizations must invest in continuous real-time monitoring, security measures, and training to ensure the safe and responsible use of AI and GenAI.