How AI is Shaping Supply Chain Cybersecurity in Times of Rising Threats

We can all agree that AI-driven tools for predicting and mitigating risks across the supply chain are no longer optional, they are essential. Check Point Research Reports a 30% rise in global cyber attacks in Q2 2024—the largest Increase in the last two years, with supply chain vulnerabilities being a factor that heavily impacted the increase. 

Dare I say that any organization not employing AI tools to power their third-party risk management will most likely find themselves in trouble before they know it. 

Without AI’s assistance, the pressure on CISO’s and Security Managers to effectively oversee every software platform is impossible. Just consider the countless SaaS tools procured by various organization employees along with vendors and third-party providers. It’s too much! The constant influx of new technologies, partnerships, and vulnerabilities means constantly playing catch-up. Let’s not forget having to keep track of the latest threats while also ensuring business continuity. 

The challenge for CISOs and Security Managers is immense and managing all of this manually will fast-track stress to overwhelming levels. Let’s see how AI can enhance your third-party cyber risk management (TPCRM) by analyzing, in real time, the vast datasets you’re up against. 

How AI Revolutionizes Third-Party Cyber Risk Management

Before GPS systems, do you remember how you got from Point A to Point B? GPS was a game changer for drivers. It quickly analyzes countless routes to guide us efficiently to our destination. So too is AI’s ability to sift through enormous datasets, scanning voluminous amounts of data, pinpointing threats, and charting a clear path to secure the supply chain.

Today would you drive a long distance to an unfamiliar place without GPS? Most likely not.

Beyond AI’s efficiency at analyzing information from multiple sources—whether vendor activity logs, security reports, or communication patterns—AI uncovers hidden risks faster and more accurately than we mere humans ever could. 

I’m not just talking about surface-level anomalies. AI models spot subtle behavioral changes among third parties that may signal a brewing threat. You know, patterns that typically fly under the radar—until it’s too late. AI can help you flag them early enough to give you a fighting chance.  

Do you want an example? Take natural language processing (NLP). NLP is an AI-driven technology that scans supplier communications. It identifies red flags in email exchanges and contract language and can examine thousands of documents in the time it took you to read up to here in this paragraph. It will show you risks that would take a human team days or even weeks to uncover. Talk about getting a fighting chance.

Machine Learning and Continuous Risk Scoring

Similarly, machine learning can continuously update risk scores based on new data—adapting in real-time to changing conditions within your supply chain. 

ML models can process vendor performance data, threat intelligence feeds, compliance records and even news alerts about potential vulnerabilities. The model constantly learns from these inputs and the ML algorithms adjust risk scores dynamically to reflect the current security posture of each third-party vendor or software provider.

As a Security practitioner, AI can help you move from a reactive posture to a proactive one, identifying risks before they escalate into incidents.

AI helps you move from a reactive posture to a proactive one, identifying risks before they escalate into incidents.

Here’s an example. Let’s say a vendor’s system is hit by an unexpected spike in network traffic or an anomaly in user behavior. ML can instantly flag these patterns as potential threats and adjust that vendor’s risk score accordingly. 

This real-time recalibration allows you to detect shifts in risk levels before they become critical. You’ll be able to take proactive measures like limiting the vendor’s access, investigate further, or simply reach out to the vendor for clarification.

In contrast to traditional risk assessments which might occur quarterly or annually 😨, ML-driven continuous risk monitoring ensures that you have up-to-date information. Considering threats evolve daily and supply chain components can be compromised at any moment, this can give you a critical advantage. 

Find out how Panorays can help you achieve this simply!

The Challenges of Implementing AI

Of course, with any AI-driven solution, there are challenges. 

Implementing AI tools effectively requires robust data management practices and integration with existing security frameworks. One of the biggest hurdles is data quality—AI models are only as good as the data they process. So all the data coming from your third-party vendors, SaaS tools, and internal systems need to be accurate, complete, and standardized. Without clean, well-organized data, AI algorithms might deliver inaccurate risk scores or miss key threats, leading to false positives—or a false sense of security.

Panorays AI machine is fueled by a decade of data focused on TPCRM.

Another challenge of AI lies in its seamless integration into your existing security infrastructure and ecosystem. 

Like any new piece of software, AI tools need to work in harmony with other cybersecurity solutions like firewalls, SIEM systems and identity management tools. If the proper APIs are not configured properly, you could wind up with gaps in visibility or delays in detecting threats. That would defeat the purpose of implementing AI in the first place!

Finally, there’s the challenge of scaling. As your supply chain grows and new vendors come on board, your AI system must scale to process more data without compromising performance. This might require additional investments in cloud infrastructure or computing power to handle larger datasets, more frequent updates, and more complex risk assessments.

This is where a trusted SaaS partner like Panorays becomes invaluable. 

Our AI engine is powered by a decade of data that enables us to deliver Risk DNA Assessments that help Security Managers make informed decisions. Our machine learning-driven “affiliation” models provide accurate asset discovery, identifying digital assets across your third parties and their supply chains, while minimizing false positives. 

Additionally, our AI models, built on extensive historical datasets, enable breach prediction, giving you insights into the likelihood of a supplier being compromised. With our AI-powered TPCRM, you’re not just reacting to risks—you’re staying ahead of them.