[MTE] [Aug 2025] [P2] Safeguarding AI: Mitigating Data Corruption Through Business Continuity

Part 2: From Vulnerability to Resilience – Using Business Continuity to Safeguard AI

While the risks of AI corruption are concerning, Michael Nieman’s presentation also offered a path forward.

Michael's message to organisations was clear: the vulnerabilities in AI infrastructure can be transformed into strengths if approached through the lens of business continuity and resilience.

According to Michael, many AI systems today are built on fragile foundations. Outdated infrastructure, inadequate security controls, and weak data management practices create fertile ground for corruption. Human error, whether through mislabeling data or improper input management, further increases exposure.

When combined with the growing complexity of AI integration, these vulnerabilities pose a significant threat to the reliability and sustainability of AI deployments.

Yet, as Michael explained, vulnerabilities need not be permanent weaknesses. By adopting global standards such as ISO 27001 for information security, ISO 42001 for AI governance, the NIST AI Risk Management Framework, and the EU Artificial Intelligence Act, organisations can build systems that are not only compliant but also transparent and ethically sound.

These frameworks emphasise accountability, ethics, and governance—all essential for AI to gain long-term trust.

Business continuity provides the practical strategies to apply these principles. Michael emphasised the importance of regular data backups and restore testing so that organisations can recover quickly from corruption.

He also emphasised the importance of building redundancy and failover mechanisms into infrastructure, ensuring that operations are not disrupted when failures occur.

Continuous monitoring of both systems and AI prompts helps organisations detect anomalies early, before they escalate into bigger issues.

Beyond these technical safeguards, Michael highlighted the role of policy and oversight. Strategies like prompt filtering and output filtering protect AI systems from malicious instructions and harmful outputs.

Logging and auditing maintain accountability, while human-in-the-loop oversight ensures that people remain the ultimate decision-makers, not algorithms. For Michael, this last point is critical: AI can enhance decision-making, but responsibility and accountability must always rest with humans.

Interestingly, he also suggested that AI itself can become a powerful ally for business continuity.

Tasks such as document reviews, plan testing, and log analysis—traditionally time-consuming for continuity teams—could be accelerated with AI tools, allowing professionals to focus on higher-value resilience strategies.

In this way, the relationship between AI and business continuity is not just defensive but also mutually reinforcing.

Michael concluded with a reminder that AI continuity is now part of organisational continuity.

Just as disaster recovery and crisis management evolved into core components of resilience, safeguarding AI is the next step.

Organisations that embed business continuity into their AI strategy will not only minimise risks but also unlock the potential for AI to enhance its own resilience.

Summing Up Part 2 – Key Points

Dr Goh Moh Heng, President of BCM Institute, summarises this webinar. If you have any questions, please speak to the author.

Summing Up for Parts 1 & 2 ...

Click the icon on the left to continue reading Parts 1 & 2 of Michael Nieman's presentation. 

 

Safeguarding AI:  Mitigating Data Corruption Through Business Continuity

Find out more about Blended Learning BCM-5000 [BL-B-5] and BCM-300 [BL-B-3]

Please feel free to send us a note if you have any questions.