Embedding AI technologies throughout supply chain operations has the potential to reshape risk management. In fact, new data found that enterprises using unified AI platforms in their supply chain operations achieve significantly better outcomes than those using isolated solutions. As AI proves its value in supply chain management, organizations that fail to adopt it risk falling behind, especially as competitive pressure accelerates the AI arms race.
AI offers organizations numerous competitive advantages in the management of supply chain risk. It creates visibility into supplier risk, ensures regulatory compliance, contributes to due diligence processes and drives operational resilience. Despite AI being the foundation of a digital revolution across the globe, the failure to leverage AI securely has potential to bring more threats than opportunities. JPMorgan’s CISO warned of the amplification of risk introduced by applications of AI and the need for more security controls.
To fully reach the potential of AI, security needs to be at the forefront of implementation. Not only within an enterprises’ own organization, but AI security must also be integrated into third-party security strategies to minimize the risks of AI adoption throughout intertwined supply chains.
The dual-use of AI surfaces risks
On the one hand, AI is used to enhance cyberattacks. On the other, businesses are using AI to streamline processes, boost efficiencies and make more informed, data-based decisions. Both applications surface new risk for enterprises beyond concerns about generative AI model hallucinations and misinformation.
Today, hackers are utilizing AI to enhance the sophistication of cyberattacks. Researchers are increasingly discovering malicious AI models on open-source platforms, but there are additional threats stemming from data input and outputs.
The input of sensitive data into public AI models could bring about privacy and IP risks as this data is made accessible to the model developer, or even hackers who have found a way to access it. On the other hand, the ability to spread data-stealing prompt injection worms through large language models creates concerns over data output risks.
These new third-party risks stemming from AI models require strict security assessments and considerations. With the global web of interconnected supply chains, this makes it more important for enterprises to understand the third-party risk that AI adoption introduces to their supply chain.
Top 3 AI risk vectors
The current narrative around AI fails to account for the data quality and the related risks as part of the security framework. However, the reality is that AI models that are not carefully managed or securely integrated can introduce significant risks from misinformation to system failures.
The top risk vectors for enterprises to be aware of throughout their supply chains are:
