Embedding AI technology across supply chain operations can potentially restructure risk management. in fact, New data It was found that companies using a unified AI platform in supply chain operations achieve significantly better results than companies using isolated solutions. As AI proves its value in supply chain management, organizations that are unable to adopt IT risks are lagging behind, especially as competitive pressures accelerate the AI arm race.
AI provides many to organizations Competitive Benefits Managing supply chain risks. It creates supplier visibility into risk, ensures regulatory compliance, contributes to the due diligence process, and drives operational resilience. Despite AI being the foundation of the digital revolution around the world, the inability to use AI safely poses more threats than opportunities. jpmorgan's ciso I warned The amplification of risks introduced by AI applications and the need for more security controls.
To fully reach the possibilities of AI, security must be at the forefront of implementation. Not only within the company's own organization, but AI security must be integrated into third-party security strategies to minimize the risk of AI adoption across its intertwined supply chains.
Dual AI use is risky
On the other hand, AI is used to enhance cyberattacks. Meanwhile, businesses use AI to streamline processes, increase efficiency and make more informed database decisions. Both applications represent new risks for the enterprise, beyond concerns about hallucinations and misinformation of generative AI models.
Today, hackers are using AI to enhance the refinement of cyberattacks. Researchers are increasingly discovering malicious AI models on open source platforms, but there are additional threats caused by data input and output.
This data can pose privacy and IP risks for model developers, or even hackers who have found ways to access it. Meanwhile, the ability to spread prompt injection worms that steal data through large language models creates concerns about data output risk.
These new third-party risks, which stem from the AI model, require strict security assessments and considerations. The global web of interconnected supply chains makes this even more important for businesses to understand the third-party risks that AI adoption will introduce into their supply chains.
Top 3 AI Risk Vectors
The current narrative of AI cannot explain the quality of data and associated risks as part of the security framework. However, the reality is that AI models that are not carefully managed or securely integrated can pose great risks, from misinformation to system failure.
The top risk vectors for businesses to recognize through their supply chain are:
・ input: The risk of data addiction at the input level is particularly common in large language models, and the model depends on the quality of the signal. When data addiction training occurs, the model's algorithm is formed.
・ Model: Corruption or addiction of the algorithm of the model also allows an attacker to potentially inject malicious code through openly available models.
・ output: Rapid injection attacks are growing concerns that finely targeted prompts can manipulate AI model outputs.
Minimize AI risks at every stage, from input to output
So how can companies ensure that AI will not open up organizations according to risk at any of these stages?
At the input level, cleaning data is an essential component to prevent data poisoning. This is even more important for AI models that leverage public data, such as supply chain risk management applications. This ranges from simple applications that flag postcodes that do not include five numbers, for example, to postcodes that require manual intervention and scrubs to account for new regulations changes.
Like the input stage, human collaboration is required to ensure that AI algorithms and models are not corrupted. Regarding real-time mapping of supply chain risk scores, there is calibration of private signals, corporate signals, and external risk signals, with weights placed on each of these parameters. Once the score is calculated, the risk management company must work with the company to ensure that the determined score is accurate and meaningful to the customer.
Finally, as rapid injection attacks grow more commonly, bad actors can earn entry points into corporate data by calculating prompts to manipulate the model output. At every stage, businesses need to recognize the risks of AI opening doors to introduce flaws, vulnerabilities, or other operations into their supply chain.
Managing risks across the supply chain ecosystem
Today, it's not just the physical and digital supply chains that need to be protected. The AI supply chain also needs to be fixed from inputs to algorithms to outputs. Today, CISOs need to pay close attention to AI risks as technology is adopted at a fierce pace across the organization.
From a federal government perspective, there is an urgency for government agencies leading AI governance frameworks using clear guardrails to prevent the harmful effects of AI. Nevertheless, it is also important that companies visualize the AI tools they are employed within their organization. Additionally, understanding how direct and indirect partners and suppliers use AI in their operations can help businesses recognize potential vulnerabilities within their supply chains.
Promote supply chain resilience by protecting AI
Cyber incidents in today's digital age are no longer isolated incidents. They affect many businesses downstream of the breached organization. To completely lock down AI possibilities in the supply chain, security must be prioritized from the start. With each organization using AI, a strong security framework is essential to managing and safely expanding its own risks.
