Key reasons third-party risk management programs fail

• According to a Gartner report, over 80% of organizations have encountered business disruptions due to third-party issues in the past two years, highlighting the importance yet difficulty of managing such risks.
• One of the key reasons TPCRM programs fail is their reliance on traditional methods like questionnaires, which are often slow, resource-intensive, and provide limited assurance due to their dependence on third-party self-attestation.
• Artificial Intelligence (AI) is a potential solution to improve ROI in TPCRM. AI can automate risk assessments, analyze vast datasets for efficient risk identification, and provide predictive analytics for proactive risk mitigation.

John P. Mello Jr.’s article discusses organizations’ prevalent challenges in managing third-party cybersecurity risks and offers insights into developing an effective risk management program. According to a Gartner report, over 80% of organizations have encountered business disruptions due to third-party issues in the past two years, highlighting the importance yet difficulty of managing such risks. The complexity of third-party risk management (TPCRM) is attributed to factors like the proliferation of cloud services, the extensive use of vendors, and increasing regulatory demands. The COVID-19 pandemic has exacerbated these challenges by forcing employees to work remotely and reducing available resources.

One of the key reasons TPCRM programs fail is their reliance on traditional methods like questionnaires, which are often slow, resource-intensive, and provide limited assurance due to their dependence on third-party self-attestation. As enterprises increasingly depend on a vast network of third parties, managing them with manual processes becomes impractical.

Moreover, such programs often become bogged down in unwieldy processes and need more effectiveness in communication and actionable insights.

Gartner suggests four actions to enhance TPCRM effectiveness: regularly reviewing third-party risk communication, tracking third-party contract decisions, conducting incident response planning, and working with critical third parties to improve security practices. However, traditional one-size-fits-all programs must consider each third-party relationship’s unique aspects. To avoid this, implementing a tiered system for risk assessment and using system tools for monitoring is recommended.

Artificial Intelligence (AI) is a potential solution to improve ROI in TPCRM. AI can automate risk assessments, analyze vast datasets for efficient risk identification, and provide predictive analytics for proactive risk mitigation. AI-powered tools can enable continuous monitoring and dynamic access control to address potential issues promptly. Integrating AI in TPCRM is crucial for organizations to handle third-party risks effectively and ensure business resilience in an increasingly interconnected operational environment.

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