Managing AI Supply Chain Risks: 6 Steps

AI in supply chains can boost efficiency but introduces risks like data breaches, model theft, and adversarial attacks. Here's a 6-step framework to identify and mitigate these risks effectively:

1. Map AI Dependencies
   Identify all third-party AI tools, vendors, and their integration points. Document key details like data usage, access permissions, and vendor compliance.
2. Evaluate Security Risks
   Use risk assessment frameworks (e.g., NIST AI RMF) and AI-powered tools to rank risks based on vendor criticality and data sensitivity.
3. Implement Risk Reduction Strategies
   Diversify vendors, vet suppliers thoroughly, and use predictive tools to anticipate disruptions.
4. Create Incident Response Plans
   Develop clear protocols for AI-related incidents and test them regularly through simulations.
5. Monitor Supplier Performance
   Track metrics like delivery, compliance, and risk scores using AI dashboards and conduct regular audits.
6. Collaborate and Share Information
   Build strong partnerships with suppliers, communicate openly, and leverage industry resources for shared security insights.

Key Insight: With AI adoption doubling between 2023 and 2024, companies must secure their supply chains to protect efficiency gains. This structured approach helps prioritize risks and ensure long-term operational stability.

How AI Helps Key Partners Manage Supply Chain Risk

Step 1: Map Your Third-Party AI Dependencies

Start by identifying all the AI tools and systems your organization relies on. This step helps you understand the extent of your exposure to potential risks. Many businesses are surprised to find hidden or expanding dependencies on AI.

As ISACA Member Dooshima Dabo'Adzuana points out:

"When third-party AI tools are introduced, you are extending your risk exposure deep in the supply chain. Risk is no longer only upstream; it cascades downstream as well and can impact clients, regulators, and national infrastructure, depending on your sector."

This ripple effect means that even one overlooked AI dependency could create vulnerabilities that impact your entire operation.

Document AI Vendors and Tools

To start, conduct a thorough review of your current AI landscape. Traditional risk models often miss the complexity of modern AI systems, where a single product might involve multiple third-party providers. Build a detailed inventory of all AI tools, platforms, and services your organization uses. Don’t stop at the obvious tools - many standard software solutions now include AI capabilities that can evolve over time, introducing new dependencies.

For each AI tool, collect and document key details, such as:

• Vendor name and contact information
• AI capabilities being utilized
• Types of data being processed
• Integration points with other systems
• Access levels and permissions
• Contract terms and renewal dates

Pay close attention to how data flows through these tools and where it’s processed. Knowing where your data is stored and how it’s handled can help you identify potential vulnerabilities. Many AI vendors use cloud infrastructure from multiple providers, creating additional layers of dependency that need to be tracked.

Don’t forget to investigate secondary suppliers. Your direct vendors might rely on other AI tools or services, which can introduce hidden risks into your supply chain. Platforms like AI for Businesses can be helpful in identifying AI tools within your industry, offering insights into your own systems and those your vendors might be adopting.

Once you’ve cataloged your AI tools, the next step is to evaluate how these dependencies affect your overall risk profile.

Define Vendor Relationships

Using your inventory, categorize vendors based on their importance to your operations. Classify them from low-risk to mission-critical. For instance, a vendor providing AI-driven marketing analytics poses a different level of risk compared to one managing financial transactions or customer data.

Map out the integration points between each vendor’s AI systems and your operations. These touchpoints are often where vulnerabilities arise, especially when data is transferred or AI-driven decisions trigger automated actions.

It’s also essential to assess each vendor’s governance and compliance practices. Before committing to a vendor, evaluate their financial stability, security measures, compliance with regulations, and data handling protocols. Ask specific questions like:

• How do you ensure transparency, fairness, and accountability in your AI systems?
• What security measures are in place to protect our data?
• How do you stay compliant with current and upcoming regulations?
• What is your protocol for handling data breaches or system failures?

Document the performance metrics and monitoring capabilities that each vendor offers. Having real-time visibility into system performance, data access, and compliance adherence is critical for managing risks. Vendors who cannot provide this level of transparency may pose a greater risk to your supply chain.

Finally, establish clear communication and escalation procedures for each vendor. If an AI system fails or a security breach occurs, you’ll need immediate notification and a rapid response plan. Strong communication channels can make a significant difference in how quickly you can address and resolve disruptions.

This mapping process is the foundation for effective risk management. Without a clear understanding of your AI dependencies and vendor relationships, you’ll struggle to assess risks, implement controls, or respond to incidents effectively.

Step 2: Evaluate and Rank Security Risks

Once you've created your AI dependency map, it's time to dive into the next step: evaluating and ranking the security risks tied to third-party integrations. Not all vendors carry the same level of risk, so understanding their criticality is key to directing your resources wisely. To streamline this process, structured frameworks can help you standardize risk assessments across all your AI vendors.

Apply Risk Assessment Frameworks

Using a structured framework ensures consistency when evaluating risks. One of the most widely recognized options in the U.S. is the NIST AI Risk Management Framework (AI RMF). This framework emphasizes governance, transparency, accountability, and fairness, making it a solid choice for organizations across industries. However, smaller teams may find its implementation challenging due to the complexity and resources required.

Other frameworks offer different strengths. For example, Microsoft's AI Security Framework integrates seamlessly with Microsoft's ecosystem, focusing on operational security. The MITRE ATLAS Framework, on the other hand, catalogs real-world AI threats and attack techniques, making it highly practical for understanding specific vulnerabilities. There’s also the Databricks AI Security Framework, which is platform-agnostic and bridges business needs with security concerns, though it's newer and less widely adopted.

When assessing risks, tailor your approach based on a vendor's criticality and the sensitivity of the data they handle. High-risk vendors should undergo detailed security evaluations, including audits of their controls and compliance certifications. For lower-risk vendors, adhering to standard industry practices and maintaining clear incident reporting protocols may suffice. To ensure consistency, use structured questionnaires that cover key areas like data encryption, access controls, incident response plans, and regulatory compliance.

Once your frameworks are in place, consider leveraging AI tools to simplify and enhance the assessment process.

Use AI-Powered Risk Tools

AI tools can make evaluating third-party risks faster and more accurate by automating many of the manual tasks involved. For example, Centraleyes offers an AI-powered risk register that maps risks to controls within established frameworks, boosting both efficiency and precision. These tools are especially useful when managing a large number of AI vendors.

Other examples include IBM watsonx.governance, which provides explainability and risk scoring, and CalypsoAI, designed for real-time monitoring of generative AI usage. The right tool for your organization will depend on your specific needs and existing technology stack. Keep in mind that while traditional GRC tools focus on policies and audits, AI governance requires deeper visibility - such as model-level insights, prompt monitoring, AI-specific controls, and integration with cloud-native services.

To further strengthen risk management, consider using Software Composition Analysis (SCA) tools. These tools analyze the components and dependencies in your AI systems, helping to identify vulnerabilities in third-party integrations. Additionally, require the creation of AI Software Bills of Materials (SBOMs) for all AI software assets. SBOMs provide a detailed inventory of system components, giving you the transparency needed to manage risks effectively.

The ultimate aim of this step is to create a clear, prioritized list of risks. This allows you to focus your mitigation efforts where they matter most. With over 60% of enterprises expected to incorporate generative AI into critical business processes by 2024, addressing the most impactful risks should be your top priority.

Step 3: Put Risk Reduction Strategies in Place

Once you've ranked and prioritized risks, the next move is implementing strategies to minimize those risks and strengthen your supply chain’s ability to handle disruptions. This phase focuses on managing suppliers wisely and using predictive technologies to anticipate problems before they arise.

Diversify and Vet Suppliers

Relying on a single vendor is risky business. Putting all your eggs in one basket leaves you vulnerable to security breaches, service interruptions, and compliance failures. Nearly 47% of global supply chain executives consider their businesses at risk of disruption, making diversification a necessity, not a luxury.

Start by pinpointing critical AI functions that depend on just one vendor. Reducing this reliance minimizes exposure to the vulnerabilities identified earlier. Establish partnerships with alternative vendors to create redundancy, ensuring you have a backup if your primary vendor falters.

"The ability to monitor and analyze supply chains in real time has already unlocked considerable value for forward-thinking organizations." - Jeff Alpert, founder and CEO of Pillar AI

Also, assess the geographic spread of your suppliers. Concentrating vendors in one region can amplify risks from political or environmental instability. Consider shifting some services closer to home (onshoring) or to nearby regions (nearshoring) to reduce reliance on distant suppliers.

When exploring new AI vendors, go beyond surface-level checks. A thorough vetting process should include input from IT, data science, compliance, legal, and business teams to ensure all angles are considered. Develop a standardized questionnaire covering essential topics like data security, regulatory compliance, governance, ethical practices, and risk management.

Ask vendors for solid evidence such as audit reports, certifications, or live demos. Whenever possible, run pilot tests with sample data to uncover any practical challenges before fully committing.

Your contracts should also reflect specific AI risk management needs. Don’t settle for off-the-shelf agreements - include clauses addressing data handling, incident responses, compliance monitoring, and performance standards. Plan for ongoing vendor evaluations from day one, setting up clear benchmarks and reporting systems to track their performance over time.

Use Predictive Analytics for Risk Prevention

Predictive analytics takes risk management from reactive to proactive. By analyzing historical and real-time data, these AI-driven tools can foresee potential disruptions before they hit your operations. Companies using predictive analytics in their supply chains often achieve an average 20% cost reduction and a 10% revenue boost.

Building on earlier risk assessments, predictive tools help you tackle emerging threats before they escalate. These platforms monitor critical factors like supplier financial health, cybersecurity risks, sustainability metrics, geopolitical shifts, and operational stability, offering early warnings that let you act swiftly.

Automated risk scoring systems are particularly effective. They evaluate suppliers on factors like financial stability, compliance, and cybersecurity, flagging underperformers and enabling smarter decisions about renewals or alternative sourcing.

Predictive analytics also streamlines regulatory compliance by tracking global changes and identifying vendors falling short on ESG or other requirements. This automated approach not only reduces manual work but also provides more thorough risk oversight compared to traditional methods.

Look for predictive tools that offer continuous, real-time monitoring and can integrate seamlessly with your existing systems. These platforms should deliver actionable insights and support automated decision-making when risks are detected.

Keep in mind that building a resilient supply chain might mean accepting higher costs in some areas to ensure long-term stability. It’s not always about finding the cheapest option - it’s about creating a supply chain that can adapt to disruptions without compromising operations.

With the global Predictive AI Market projected to hit $108.0 billion by 2033, these tools are becoming more advanced and accessible. Start with platforms offering clear ROI metrics and compatibility with your current systems, then expand your predictive capabilities as you see results.

These strategies lay the groundwork for ongoing supplier performance monitoring, which is the focus of the next step.

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Step 4: Create and Test Incident Response Plans

Even with measures like diversified suppliers and predictive analytics in place, preparing for potential AI-related incidents is essential. No matter how robust your risk reduction strategies are, incidents can still happen. When they do, having a well-documented and tested response plan can make the difference between a small hiccup and a full-blown crisis. Companies with formal incident response plans and teams save, on average, $473,706 in breach costs.

The numbers don't lie - supply chain breaches have surged by 40% compared to 2023, and AI-specific attacks are becoming more advanced. Take the 2025 SolarTrade incident, for example. Hackers exploited a logistics management platform used by over 500 retailers by injecting malicious AI code during a routine software update. The result? Access to customer payment details and months of disrupted supply chain operations.

Build Response Protocols

Crafting effective response protocols for AI incidents requires a fresh approach. Unlike traditional security breaches, AI-related incidents can lead to unique challenges like individual harm, algorithmic bias, regulatory violations, and disruptions that ripple across the supply chain.

Start by assembling a dedicated AI incident response team. This team should bring together technical experts, legal advisors, communication specialists, and representatives from key business units. AI incidents often demand a different playbook than traditional cybersecurity events, so having a cross-functional team is crucial.

Your protocols should address the full spectrum of AI-related risks. Include clear escalation paths and decision-making frameworks. In some cases, you may need to implement a "kill switch" to shut down an AI system entirely and prevent further damage.

Communication is another critical piece. Ensure contact information for all AI vendors is up to date, and outline notification requirements for every region where your company operates. This includes specific reporting guidelines for AI-related regulators and any organizations involved in your AI supply chain.

The response plan should also specify immediate steps, like investigating the issue, recalling affected products, and pinpointing the source of any algorithmic errors. Keep thorough documentation of your AI models and system components readily accessible.

Modern tools can provide a significant edge during incident management. Technologies like Security Information and Event Management (SIEM), Security Orchestration, Automation and Response (SOAR), and Extended Detection and Response (XDR) systems can accelerate threat detection and automate initial response actions. AI-powered security solutions, in particular, offer real-time insights that can guide response teams through critical decisions.

Once your protocols are in place, the next step is to test them rigorously and update them as needed.

Test and Update Plans Regularly

A response plan isn't worth much if it hasn't been tested. Conduct regular tabletop exercises - at least once a year - to simulate AI-related incidents. These scenarios help uncover weaknesses in your procedures and ensure every team member knows their role when an actual incident occurs.

Vary the scenarios to cover a range of potential issues, such as ransomware, DDoS attacks, insider theft, system misconfigurations, compromised AI updates, and vendor outages. Testing against different threats ensures your plan is comprehensive and flexible enough to handle unexpected challenges.

After every exercise or real-world incident, hold a post-incident review. Gather all relevant documentation, including timelines, actions taken, and communications with stakeholders. Use this review to pinpoint what went well and where improvements are needed. From there, create actionable recommendations to close any gaps.

Given how quickly the AI landscape changes, your plans need to keep up. Reassess and update your incident response protocols annually at a minimum - or whenever significant changes occur in your IT systems, business structure, or regulatory environment. Staying current with evolving AI regulations is critical for maintaining compliance.

Lastly, maintain detailed records of all incident response activities. These records not only help with compliance but also provide valuable insights that can strengthen your response capabilities over time.

Effective incident response is about more than fixing technical issues. It’s about coordinating people, processes, and technologies to minimize harm and prevent future problems. Regular testing and updates ensure your plans remain effective, setting the stage for continuous monitoring in the next step.

Step 5: Monitor Supplier Performance Continuously

Once your protocols are in place, keeping a close eye on your AI suppliers is critical. With 98% of organizations globally connected to at least one breached third-party vendor, consistent monitoring isn't just a precaution - it's a necessity.

AI vendors bring challenges that go beyond traditional supplier relationships. Risks can shift quickly due to algorithm updates, model drift, or changes in regulations. A supplier that meets standards today might become a liability tomorrow. This is why continuous monitoring forms the backbone of managing AI supplier relationships effectively.

Set Up Metrics and Dashboards

The first step in monitoring suppliers effectively is identifying the right metrics to track. Today, supplier performance isn't just about cost or quality; it also involves risk and long-term reliability metrics to give a complete picture of a supplier's health.

Start by categorizing risks into key areas: financial, operational, compliance, cybersecurity, and reputational risks. Then, define specific metrics under each category that align with your business objectives and regulatory needs.

AI-powered dashboards have completely changed how businesses keep tabs on supplier performance. These tools pull data together for real-time insights, helping with spend analysis, risk management, and performance tracking. Platforms like Tableau, Microsoft Power BI, Qlik Sense, and Looker are among the popular choices. The growing demand for such solutions is evident, with the procurement software market projected to hit $32.3 billion by 2031.

"Forward-thinking organizations are turning to third-party data and real-time technologies to transform supplier risk management. These tools deliver actionable insights to preempt escalating risks."

• Rahul Asthana, PhD in Operations Management, Gainfront

When deploying AI dashboards, set clear goals and KPIs from the outset. Involve key stakeholders to ensure the tools meet their needs, and prioritize platforms with strong data security features. Real-time monitoring combined with predictive analytics helps you spot and address potential issues before they escalate.

AI agents also play a key role, automating data analysis, spotting trends, and sending alerts for critical changes. This automation frees your team to focus on strategic decisions rather than getting bogged down in manual data collection. Regularly validating these insights through deeper audits ensures accuracy and adds another layer of security.

Perform Regular Audits

While dashboards provide ongoing oversight, audits offer a deeper dive into supplier operations. Regular audits help ensure AI systems meet ethical and operational standards, addressing issues like discrimination, privacy violations, and inefficiencies.

For AI suppliers, audits are particularly important. They confirm compliance with regulations like the EU's AI Act and reassure stakeholders about the safety and reliability of AI systems.

One example of the impact of AI-powered auditing comes from a multinational bank that implemented a compliance auditing tool. Over six months, the system analyzed data from banking systems, transaction logs, and risk databases using machine learning trained on historical breach data. The results? A 40% reduction in audit cycle time, 30% fewer false positives, and a significant boost in regulatory confidence.

Similarly, a healthcare provider used an AI-driven audit solution to monitor access logs, system usage, and data transfers. Advanced NLP flagged irregularities in unstructured data, leading to real-time alerts that cut response times to potential breaches by 50% and improved compliance reporting accuracy by over 35%.

When planning audits, start by setting clear objectives and defining the scope based on business goals, supplier performance criteria, and contract terms. Focus audits on high-risk suppliers, new partnerships, or those with performance issues. Preparation is key - review past audits, performance data, and relevant documentation beforehand.

For complex areas, consider hiring neutral third-party inspectors. Their objectivity and expertise can uncover issues your internal team might miss.

"AI agents are not just a step forward; they represent a seismic shift in conducting audits, assessing risks, and managing compliance."

• Trent Russell, Founder, Greenskies Analytics

A January 2025 poll showed that 50% of internal auditors believe AI agents provide the greatest benefit in controls testing and fieldwork, followed by risk assessment (20%), planning (19%), and reporting (11%). These findings highlight where AI can make the biggest difference in auditing processes.

Effective audits also pave the way for improved supplier relationships. Share audit findings with suppliers and collaborate on action plans to address issues. Clear communication about expectations helps ensure alignment on quality, timelines, and costs. This partnership approach can drive meaningful improvements.

As the AI landscape evolves, so must your audit processes. Adapting to new regulations and emerging risks ensures your suppliers remain aligned with your risk tolerance and compliance needs.

Step 6: Work Together and Share Information

Tackling AI supply chain risks isn’t a solo effort - it’s all about collaboration. A weak link in one organization can ripple across the entire chain, making partnerships and open communication critical. By building on the earlier steps, you can help create a more secure and resilient ecosystem for everyone involved.

Build Partnerships, Not Transactions

Treat suppliers as true partners rather than just service providers. When suppliers feel like valued collaborators, they’re more likely to invest in security measures that benefit both sides. This approach ties back to earlier steps like risk mapping and constant monitoring, creating a stronger foundation for managing shared risks.

Communicate Openly with Suppliers

Strong supplier relationships start with clear, open communication from day one. Set the tone early by discussing cybersecurity expectations, incident response protocols, and vulnerability management before signing any agreements. This ensures that suppliers understand your security needs upfront and can align their practices accordingly.

"Transparency makes smaller organizations more viable as partners".

Regular conversations are key to staying ahead of potential risks. Schedule monthly or quarterly reviews with key AI suppliers to address topics like new security requirements, system updates, or regulatory changes. These meetings can help identify emerging risks and ensure everyone is on the same page.

When security incidents happen, having pre-established notification protocols with suppliers can make all the difference. Quick, transparent communication allows for faster problem-solving and resolution. It’s also a good idea to provide your team with training on topics like data privacy, secure coding, and incident response. This not only boosts internal expertise but also leads to more productive discussions with suppliers.

Leverage Industry Resources

Beyond direct supplier communication, tapping into industry resources can significantly enhance your defenses. Collaborative platforms and shared tools provide access to threat intelligence and best practices that the broader business community has developed.

For small and medium-sized enterprises (SMEs), directories like AI for Businesses offer a curated list of vetted AI tools and vendors, including options like Looka, Rezi, Stability.ai, and Writesonic. These resources can be a helpful starting point for finding reliable suppliers.

But collaboration shouldn’t stop at finding vendors. Sharing information across organizations is vital for strengthening supply chain security. While this can raise concerns about confidentiality or legal risks, fostering a positive and proactive environment for information sharing can lead to stronger defenses for everyone.

"Improving the quality and volume of information sharing among the federal government and private industry is necessary to obtain actionable information that could mitigate threats to the Nation's ICT supply chain." - CISA

Consider joining industry-specific security groups or consortiums. These organizations often provide frameworks for secure information sharing, standardized assessments, and collaborative responses to incidents. Larger companies, in particular, can play a leadership role by mentoring smaller suppliers, sharing anonymized threat intelligence, or contributing to joint security initiatives.

Use Technology to Support Collaboration

Technology can make collaboration easier and more secure. Platforms with strong access controls, like role-based access control (RBAC) systems, help ensure that sensitive information is shared appropriately and only with those who need it.

Active participation in industry forums and collaborative initiatives is essential for staying informed about new risks and strategies. As AI evolves, so do the threats, making it crucial to remain engaged and adaptable.

Summary of the 6 Steps

Tackling AI supply chain risks requires a forward-thinking strategy to stay ahead of potential disruptions. This six-step framework lays out a clear path to pinpoint weak spots, set up protective measures, and create operations that can handle unexpected challenges. Each step builds on the last, forming a solid defense system that’s ready for anything.

Taking a structured approach to managing supply chain risks helps businesses deal with obstacles more effectively. The last two steps in the framework stress the importance of ongoing monitoring and open communication. These steps highlight that securing a supply chain isn’t something you do once - it’s a continuous process. Real-world examples show how companies have successfully weathered geopolitical crises and natural disasters by planning strategically.

One key takeaway is finding a balance between keeping costs low during normal operations and managing higher expenses when faced with major disruptions. Businesses also need to prepare for potential risks in both physical and digital supply chains. As Peter Drucker famously said, "Culture eats strategy for breakfast", emphasizing the need for a company-wide commitment to a security-first mindset.

These insights pave the way for deeper conversations about building resilient AI strategies.

Main Points for SMEs

Small and medium-sized enterprises (SMEs) face their own set of challenges when it comes to managing AI supply chain risks. However, this six-step framework is flexible enough to be tailored to their needs. Each step translates into practical actions that even resource-limited SMEs can implement.

Start by focusing on your most critical AI dependencies, then gradually expand your risk management efforts as your resources grow. Effective supply chain risk management is crucial for staying competitive. SMEs should take a close look at their supply chains, identify potential vulnerabilities, and apply appropriate solutions.

For SMEs aiming to boost their AI capabilities while keeping security top of mind, resources like AI for Businesses can connect them with trusted AI tools such as Looka, Rezi, Stability.ai, and Writesonic.

Resilience, agility, and sustainability are the cornerstones of a modern, efficient supply chain. By following this six-step framework, SMEs can embed these qualities into their operations, improving efficiency and preparedness. The goal isn’t to eliminate every possible risk but to understand potential threats, prepare for them, and respond effectively when disruptions arise. These principles are essential for building a future-ready supply chain.

FAQs

What AI tools can businesses use to assess and manage supply chain security risks?

AI-powered tools are transforming how businesses identify and manage supply chain security risks. For example, ThroughPut AI enhances decision-making by analyzing data patterns and forecasting potential disruptions. Meanwhile, Resilinc uses AI to monitor vulnerabilities and deliver real-time risk assessments. Another standout, Interos, employs advanced AI to map, visualize, and assess risks across intricate supply chain networks.

With these tools, businesses can tackle challenges head-on, ensuring operations run smoothly and supply chains remain resilient.

How can SMEs manage AI supply chain risks effectively while working with limited resources?

Small and medium-sized businesses (SMEs) can tackle AI-related supply chain risks by taking a practical and budget-friendly approach. Start by exploring affordable AI tools that help with risk assessment and improve supply chain visibility - without the need for hefty infrastructure investments. Look for solutions that can grow with your business and meet your specific needs.

Partnering with trusted AI vendors who offer customized, cost-effective options is another smart move. These partnerships can provide access to the right tools and expertise to strengthen your supply chain. On top of that, make the most of the digital technologies you already use. Gradually implement improvements to monitor and address risks over time, focusing on steady progress.

By following these strategies, SMEs can manage AI supply chain risks effectively while keeping their resources in check.

Why is it important to regularly audit AI suppliers, and what should these audits focus on?

Regular audits of AI suppliers are crucial for spotting and managing risks like bias, privacy violations, and regulatory non-compliance. These reviews play a key role in making sure AI systems operate in a way that's transparent, ethical, and aligned with your organization's principles.

When auditing, focus on these critical areas:

• Data privacy: Confirm that sensitive information is securely handled and protected.
• Algorithm fairness: Identify and address any biases within AI models.
• Regulatory compliance: Ensure the systems meet legal and industry-specific standards.
• System transparency: Verify that AI decisions and processes are clear and easy to understand.

Thorough audits not only help mitigate risks but also strengthen trust with stakeholders while protecting your business from potential AI-related challenges.

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