by Monika Atanasova

There are 3 main kind of systems:

-1 – basic automation: these systems do simple, repeated jobs based on fixed rules. They are good at doing one thing well, but can`t change their plan, understand the situation, or learn new things. Examples are simple programs for typing data or making backups. They need people to step in when something unexpected happens.

-2 – connected systems: these systems are better. They can share information across different computers and data sets. People still need to guide their decisions and changes. But because they can use more data, they make smarter choices and work better than basic systems. They can find patterns and unusual things. But people are still needed to understand these findings and start actions.

-3 – autonomous AI: this is the newest AI. Smart agents work mostly on their own. They can understand complex situations, set their own goals, plan steps, do them, and keep learning from what happens. For security these agents can watch, check threats, find weak spots, and act fast to fix them across whole supply chains. They do this without constant human help, making systems much stronger and better at predicting problems.

This change means supply chain security moves from just reacting to problems to actively predicting and fixing them. This will greatly change how we manage risks with suppliers.

AI Agents have officially 7 main abilities to get things done.

1.     Goal setting: agents take your requests and make their own plans. They figure out what steps to take without you spelling everything out.

2.     Reasoning: they think through problems using available information.  When faced with choices, they can pick the best option based on what they know.

3.     Tool usage: agents can use software, look up information in databases, and connect to online services to help solve problems.

4.     Memory: they remember what happened before. This helps them understand context and build on previous work without starting over.

5.     Workflow: agents can handle multi-step tasks on their own. They follow logical steps and can adapt when things don`t go as planned.

6.     Learning: they get better with experience. Each interaction teaches them something new that helps improve future performance.

7.     Action: agents don`t just make plans – they carry them out. They take concrete steps toward finishing the job you gave them.

8.     And the 8^th component is US (still) HUMANS needed.

Now that we understand the evolution of AI Agents, let`s dive deeper into their specific and strategic role in enhancing supply chain security. Research from McKinsey and Company indicates that by 2025, organizations effectively integrating AI into their cybersecurity strategies will gain distinct competitive advantage, demonstrating 15-20% greater resilience against cyber threats compared to less AI mature competitors. There are three main use cases, where Agentic AI can help and provide solid solutions.

1. Automated Threat Detection and Response

Agentic AI systems continuously monitor the entire digital supply chain, processing billions of data points across all vendor networks. They automatically identify anomalous behaviors, such as unauthorized data exfiltration attempts, sophisticated phishing campaigns targeting third party vendors, or unusual access patterns from previously unknown IP addresses. Upon detection these agents can instantly initiate automated responses, including isolating compromised endpoints, revoking suspicious access credentials, or alerting relevant security teams. This capability dramatically reduces the Mean Time to Detect from days or hours to more seconds, significantly minimizing potential damage.

•       Autonomous AI will detect cyber threats faster, providing real-time defensive countermeasures against adversarial attacks.

•       AI-powered SynthAI security bots will manage incident response, reducing workload for cybersecurity professionals.

•       AI will generate security playbooks and execute autonomous threat mitigation strategies helping to automate SOC operations.

2. Proactive risk prediction and mitigation

Leveraging vast datasets, including historical security incidents, global vulnerability databases, and real time network traffic analyses, agentic AI can accurately predict where future vulnerabilities might emerge. They pinpoint misconfigurations in multi-cloud environment, identify outdated software versions in third-party systems, or detect subtle, anomalous user behaviors that could escalate into critical security weaknesses. This shifts your security posture from reactive incident response to proactive threat prevention, allowing you to prioritize and address the most critical risks before they are exploited.

3. Automated compliance and governance enforcement

Agentic AI is improving in the aria of continuous adherence to complex regulatory frameworks such as ISO 27001, NIST CSF, GDPR, and organization`s internal security policies across thousands of diverse suppliers simultaneously. They automatically can verify granular access controls, enforce data encryption standards, ensure timely software patch levels, and generate audit ready compliance reports in real-time. This provides in future an unprecedented scale of continuous security validation that human teams cannot realistically achieve, ensuring consistent compliance and reducing audit overhead. It should be mentioned that currently compliance is not possible without human assessors.

To fully leverage the transformative potential of Agentic AI, organizations need a clear understanding of where they stand and where they need to go. This requires a structured approach to assessing and advancing their capabilities.

There is Agentic AI Maturity Model, a framework designed to guide through the evolutionary stages of integrating AI for enhanced supply chain security, from foundational steps to fully autonomous, predictive defense.

No organization has fully reached level 4, however, several pioneering companies are actively developing and deploying core components of this visionary stage. Let`s have a look into the different stages:

Level 0=security is mostly done by people, with very little automation. They check supplier risks by hand using spreadsheets and talking to them. This means it takes long time to find problems. They do not have a clear, up to date view of risks from their suppliers, so it`s hard to stop problems before they happen. Most companies start here, only reacting to cyber threats.

Level 1=companies at this level use basic automation for simple security jobs, like checking for known vulnerabilities or sending alerts about specific threats. But these systems work on their own and follow strict rules. They can`t change easily or work with other systems. Risk scores are usually fixed, based on basic rules, not on new threats. About half of large companies are at this stage, dealing with security information that isn`t connected. Approx. 50% of Fortune 500 currently at this stage.

Level 2= AI provides insights while humans retain authority over decisions. Machine Learning identifies patterns across supplier data. Predictive alerts flag potential issues. Interactive dashboards visualize risk. Growing Sector (23% of enterprises).

Level 3=AI Agents trusted for specific automation tasks. Continuous supplier monitoring with automatic remediation of known issues. Anomaly detection drives automated responses within defined parameters. Human oversight for exceptions. Emerging capability (7% of enterprises).

Level 4=Agents manage end2end cyber and operational decisions within minimal oversight. Self-improving risk models. Autonomous incident response and recovery. Cross-enterprise coordination of AI agents. Strategic human governance. Future state (2-5 years). At this level human jobs change to strategic oversight, focusing on guiding the AI, making sure it keeps getting better, and driving new ideas.

Best practices and recommendations for implementation:

1. Start with shared control

Begin by having AI suggest actions while people still make the final decisions (this is like levels 2-3 of AI maturity).

89% of susscessful projects keep this human-AI team approach for important tasks until they trust the AI enough.

2. Connect everywhere

Use AI to monitor all digital interactions with suppliers, from when they join to when they leave (e2e).

Companies that only monitor parts of their system are 43% less effective than those that monitor everything.

3. Explain AI choices

Make sure you can understand why the AI makes certain decisions. This is important for rules and checks.

76% of leaders say clear expectations are key for boards to accept these automated security systems.

4. Keep training the AI

Regularly update the AI models with new threats, company rules, and legal changes.

Companies that update their models every three months are 57% better at finding threats than those that update once a year.

One major drug maker`s AI security system first failed because supplier information was spread out and messy. After they organized all their data in one place their AI`s ability to spot problems jupoed from 61% to 94% in just 90 days. Clean, consistent data is the basis for strong AI security.

What's next?

Emergence: Smarter Monitoring Begins

In the early stage, companies start using agentic AI to help monitor their suppliers and partners more effectively. Instead of relying on manual checks or excel spreadsheets, these AI systems can automatically scan documents, track compliance, and spot unusual behavior. They act like smart assistants—helping human teams notice risks faster and cover more ground.

For example, if a supplier forgets to update their security software, the AI can flag it immediately. This helps companies catch issues early, before they turn into bigger problems. At this point, the AI still needs human supervision, but it’s already making the process quicker and more reliable.

Acceleration: Real-Time Protection and Decision-Making

As agentic AI gets more advanced, it starts making decisions on its own. These systems can predict when a supplier might face a cyber issue, run simulations to test different risk scenarios, and even respond to threats in real time. If a vendor’s system is compromised, the AI might isolate the threat, alert the right people, and suggest a fix—all without waiting for human input.

This phase marks a big shift: companies move from reacting to problems after they happen to preventing them before they occur. The AI can evaluate hundreds of risk factors, monitor global events, and validate security controls automatically. It’s like having a team of digital experts working around the clock.

Maturity: Self-Managing, Resilient Ecosystems

At full maturity, agentic AI systems become deeply integrated across the entire supply chain. They don’t just monitor and respond—they collaborate with other AI systems across different companies, adapt to changing regulations, and even fix vulnerabilities on their own.

Cybersecurity becomes smarter and more flexible. These AI agents manage compliance, conduct audits, and maintain visibility across all partners and vendors. Instead of relying on manual controls, companies use AI to guide strategy and build stronger, more resilient networks.

In this future, supply chains aren’t just managed—they’re orchestrated by intelligent systems that continuously optimize for safety, performance, and trust.

Road Map example:

Immediate (0–6 Months): Start Smart and Small

Begin by identifying the most critical areas in your supply chain—places where a cyber issue could cause serious damage. These might include your top-tier suppliers, software vendors, or infrastructure partners. Set up pilot projects using agentic AI to monitor these areas. The goal is to test how well AI can spot risks, flag unusual behavior, and support your team. Create small, cross-functional teams that bring together cybersecurity experts, supply chain managers, and AI specialists. These teams will guide the pilot and learn what works.

Near-Term (6–18 Months): Build and Expand

Once the pilot shows results, start expanding. Add more suppliers and systems to your AI monitoring network. Develop clear ways to measure success—like how fast your team responds to threats, how well AI predicts problems, and how much risk is reduced. Train your AI systems to handle more complex tasks, like running “what-if” scenarios or suggesting backup plans when a supplier looks risky. Make sure your data systems are ready—AI needs clean, real-time data to work well.

Strategic (18–36 Months): Scale and Integrate

Now it’s time to go big. Use agentic AI across your entire supply chain. Let multiple AI systems work together across departments and even with partners. Focus on building a supply chain that can adapt on its own—where AI can detect problems, fix them, and keep things running smoothly with little human help. Use AI to manage compliance, run audits, and stay ahead of changing regulations. Make agentic AI part of your company’s long-term strategy. Include it in board-level discussions and invest in the talent and tools needed to support it.

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