Threat actors are leveraging AI for everything from hyper-realistic phishing schemes to deepfake impersonations, synthetic identity creation, and autonomous intrusion attempts. While this is a threat to your own organization, it’s also opening up threats in the supply chain.

These attacks don’t arise in a vacuum. They often exploit vulnerabilities within an organization’s third-party vendor ecosystem. As such, third-party risk management has emerged not only as a compliance function but as a critical pillar of cybersecurity in the AI era.

The AI-Enhanced Cyber Threat Landscape

AI is cranking up both the sophistication and scale of cybercrime in scary ways. Bad actors are now using generative AI to write phishing emails that look so real you can’t tell them apart from legitimate human communication. Deepfakes (fake audio, images, or videos) are being weaponized to impersonate executives and trick people into authorizing bogus financial transfers. Meanwhile, automated bots are hunting for system vulnerabilities with a precision and speed that leaves human hackers in the dust.

What’s really concerning is the explosion of fake identities. When attackers combine these fake identities with access they’ve gained through a trusted third-party provider, they can waltz right past even your strongest internal security controls, as if they owned the place.

Why Third-Party Ecosystems Are Targeted

Most companies today can’t function without their army of third-party providers, including cloud platforms, SaaS tools, data processors, and MSPs. These vendors have become so embedded in daily operations that their connection to crucial functions makes them ripe targets for attack.

This lack of visibility makes vendors prime targets for attackers looking for backdoor access to your systems. When a vendor starts using AI models without proper guardrails or oversight, things can go sideways fast. They might accidentally misuse your customer data, make biased decisions that hurt your business, or create openings that hackers can exploit automatically. Even vendors you trust completely can get compromised without knowing it, essentially becoming unwitting accomplices in an attack.

For example, a cloud provider that utilizes AI to optimize server workloads. If their training data is flawed or their AI models are misconfigured, then that vulnerability most likely falls back on you and your data as well.

Without clear communication about their activities or ongoing monitoring to catch problems early, these issues can fly under the radar until someone with malicious intentions discovers them.

TPRM as a Compliance and Security Strategy

Traditionally, TPRM programs have been all about upfront due diligence, including processes like sending out questionnaires, scoring risks, and locking down contracts. AI is changing the effectiveness of these techniques, however, which is leading organizations to fight fire with fire. Organizations need to think of TPRM as a living, breathing, intelligence-driven defense layer that’s woven into their broader cybersecurity strategy.

Key actions include:

AI Disclosure Requirements: Mandate that vendors reveal how AI is used, what data it processes, how models are trained, and what safeguards are in place to prevent misuse or bias.
Model Explainability and Governance: Evaluate the transparency of vendor AI systems. If a vendor uses AI to make decisions that impact your organization’s security or compliance posture, those models must be explainable and auditable.
Continuous Monitoring: Move beyond point-in-time assessments. Use real-time behavioral analytics to detect anomalies in vendor operations or changes in AI behavior, such as model drift or unexpected data access patterns.
AI-Specific Incident Response Protocols: Develop contingency plans for incidents involving rogue AI behavior, synthetic content dissemination, or unauthorized data automation by third parties.

Using AI to Strengthen TPRM Programs

AI is a threat and a powerful ally in equal measure. Forward-thinking organizations are now using AI internally to enhance their own TPRM processes.

Capabilities include:

Automated Due Diligence: Machine learning models can scan public records, breach databases, and regulatory reports to flag risky vendors in real time.
Dynamic Risk Scoring: AI can generate contextual risk profiles that evolve with vendor behavior and threat intelligence, allowing security teams to prioritize response.
Behavioral Monitoring: Advanced analytics and anomaly detection tools can monitor third-party system logs and usage patterns for signs of compromise or misuse.
Natural Language Processing: NLP can review vendor contracts to highlight vague terms, especially clauses related to data sharing, liability, or AI automation.
Visual Risk Mapping: Graph databases and AI visualization tools can reveal interdependencies and hidden exposure within sprawling vendor networks.

By integrating AI into the risk management lifecycle, organizations transition from reactive assessments to proactive, pr edictive security governance.

Embedding AI Oversight Into the TPRM Lifecycle

A solid, cybersecurity-focused TPRM program needs to weave AI risk into every step of the vendor lifecycle:

Policy and Governance Alignment: Establish clear standards for vendor AI use, ensuring alignment with internal security policies, data handling rules, and ethical AI practices. Set expectations upfront about what’s acceptable and what crosses the line.
Vendor Onboarding and Evaluation: What datasets and controls do they use? Do they have their own AI security in place to combat these threats? Roll out AI-specific security questionnaires that dig into the details like these. Don’t just take their word for it; ask for the documentation to back it up.
Contractual Safeguards: Build in contract clauses that cover AI-related failures or misuse, and push for third-party audits. Set hard limits on how vendor AI systems can access or use your customer data, and make sure there are consequences when things go sideways.
Ongoing Oversight: Monitor changes in vendor behavior or AI tool updates that could introduce new risks. Use automation to track when AI systems get modified, retrained, or start performing differently than expected. Don’t rely on vendors to self-report problems.
Incident Management: Develop AI-specific incident response plans that include rapid deactivation of problematic models and clear escalation protocols for threats such as deepfakes or synthetic identity attacks. Know exactly who to call and what to do when AI goes rogue.

TPRM in the Era of Intelligent Adversaries

Third-party risk is no longer just a procurement or legal concern—it’s a frontline cybersecurity challenge. The introduction of AI into both offensive and defensive security has transformed third-party relationships into potential vectors for highly adaptive, hard-to-detect attacks.

Cybersecurity teams must modernize TPRM by:

Integrating AI risk into vendor scoring models.
Auditing not just systems, but also algorithms and data governance practices.
Advocating for secure development lifecycles for vendor AI products.
Investing in platforms that offer AI-enabled compliance automation and continuous monitoring (such as Continuum GRC).

Securing the AI-Driven Supply Chain

Organizations that elevate TPRM to a core pillar of cybersecurity, backed by AI-driven tools and agile governance, will be better equipped to identify emerging risks, reduce attack surfaces, and respond to threats at machine speed.

In an environment where intelligent, automated adversaries are already active, the security of your organization may well depend on the intelligence and resilience of your third-party risk management strategy.

To learn more about how Lazarus Alliance can help, contact us.

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Third-Party Risk Management and Defense Against AI-Driven Cyber Threats