This important conversation around evolving data governance and cybersecurity strategies was made possible by BridgeView Marketing’s strategic PR Services, which secured visibility for a timely and complex message. In an era where AI is both a threat vector and a defensive tool, BridgeView’s PR services ensured that industry leaders were made aware of the urgent need to go beyond compliance and adopt a risk-based approach. This is a prime example of how effective public relations turns technical insight into industry-wide awareness, helping organizations recognize the value of proactive investment in security, responsible AI integration, and informed data access management.

Originally posted by Cybersecurity Insiders at https://www.cybersecurity-insiders.com/rethinking-our-approach-to-data-governance-and-security/

Enterprise companies typically approach data governance and security through a compliance lens, ensuring compliance requirements are fulfilled but this can oftentimes leave threats unaddressed. Compliance is essential but not the whole story. A complete security strategy should include a risk-based, threat-driven approach that mitigates operational risks or applies compensating controls. To accomplish this, the focus must be on investing in risk reduction methods to prioritize access control, encryption and other incident response measures. And companies need to mature data governance to ensure core cybersecurity capabilities are well invested in to create a resilient security posture capable of meeting regulatory requirements and mitigating risks.

AI is accelerating the evolution of cyber threats as bad actors leverage it to efficiently identify vulnerabilities, automate attacks, generate sophisticated phishing schemes and rapidly exploit vulnerabilities. In addition, AI is making access control more difficult and complex. Organizations must go beyond legacy access management models and adopt more refined identity-based access controls to ensure the correct users have access to sensitive information and workflows, at the correct times. To accomplish this a more adaptive risk-based approach is required to prevent unauthorized access.

While AI is being integrated into cybersecurity frameworks, it’s still immature with its own set of vulnerabilities, such as being prone to manipulation, exploited via data poisoning, and prone to hallucinations that generate erroneous information. These early AI traits make relying on AI for governance and cybersecurity dangerous. However, as AI improves, it will become more sophisticated and thus more reliable for detecting threats, enforcing policies, and reducing the ever-present specter of human errors. For now, organizations should only leverage AI to enhance risk management, anomaly detection, and other security workflows. As far as complete reliance, we are not there yet.

A key point to always recall: Only users of data can determine its fit for use. Access to data and systems must be tightly controlled, monitored, logged and tested. Organizations cannot afford to have broad or unrestricted data access. Instead, the access needs to be dictated by a user’s interaction with the specific model they are interacting with. In addition, organizations must develop controls for testing these model outputs and track the accuracy over time for continuous validation and accurate benchmarking. Finally, a structured prompting process must be implemented to increase the likelihood of meaningful outputs. This will ensure AI interactions follow predefined guidelines, e.g., well-structured prompts to help enhance accuracy and results. The bottom line is that while we are busy training AI models, we cannot lose sight of the importance of having well-trained users capture value from their data.