AI is changing third-party risk management as both attackers and defenders adopt machine learning capabilities. While threat actors use generative models to target vendor networks more effectively, security teams are moving from static assessments to continuous, adaptive monitoring.

As our recent "Inbox Under Siege" webinar highlighted, traditional approaches struggle to keep pace with evolving threats that exploit supplier relationships, API connections, and communication channels. Threat actors now use AI to scale attacks across multiple vectors, from cryptocurrency fraud to multichannel phishing, with email remaining the front door to vendor compromise.

This guide demonstrates how AI enhances third-party risk management by improving vendor discovery, behavioral analysis, and automated response capabilities, creating defenses that better keep pace with threats targeting your supply chain.

AI has made vendor compromise faster and more dangerous. What once took weeks now happens in hours, turning trusted suppliers into potential entry points for large-scale fraud. According to the Verizon 2025 DBIR, 30% of breaches now involve a third party, highlighting how attackers exploit trust-based relationships between organizations and their vendors.

Imagine an accounts-payable clerk approving a routine invoice from a familiar vendor, with correct formatting and believable banking details. In reality, AI crafted the email based on past communications.

Today's attackers use generative models to mimic writing styles, deepfakes to impersonate executives, and exploit vulnerabilities in third-party software to gain access to downstream networks. They automatically scan APIs and conduct BEC attacks, with phishing as a primary access vector.

To defend against these evolving threats, require MFA on vendor portals, enforce SPF/DKIM/DMARC with domain alignment per CISA Binding Operational Directive 18-01, and use video callbacks for large payments. Most importantly, deploy AI that learns each vendor's normal behavior and flags anomalies in real time.

Avoid relying on manual invoice checks, overlooking smaller vendors, or assuming suppliers have secure email setups. Track vendor email authentication implementation across three critical standards: the percentage of vendors with valid SPF records, including authorized third-party senders, DKIM signing with documented key rotation procedures, and progressive DMARC policy enforcement.

Monitor DMARC aggregate reports quarterly to identify unauthorized senders attempting to impersonate your organization through vendor email channels.

Third-party AI tools require rigorous governance because vendors often lack adequate security controls and transparency into their systems. When a vendor's algorithm produces discriminatory outcomes or an unvetted language model leaks personal information, regulators increasingly treat the incident as the deploying organization's responsibility under frameworks like DORA and the EU AI Act.

Organizations should implement comprehensive third-party AI risk management through vendor selection standards, continuous monitoring, and contractual requirements ensuring vendors meet regulatory security baselines.

Inherited risk manifests in three critical ways:

• Biased Models: Can warp hiring, lending, or benefits decisions, triggering civil rights investigations.

• Untested LLMs: May ingest sensitive records and violate privacy statutes like GDPR.

• Excessive Data Collection: Many supplier applications collect more data than disclosed, creating stealth exposure with specific instances documented in vendor risk assessments.

Regulators rapidly increase enforcement under EU AI Act requirements. The implementation timeline includes four critical milestones:

• February 2, 2025: All AI systems classified as presenting "unacceptable risk," such as social scoring or manipulative biometric tools, face bans from the EU market.

• August 2, 2025: Binding rules for General Purpose AI (GPAI) providers take effect.

• August 2, 2026: Full compliance deadline for high-risk AI systems, including those in critical infrastructure, education, law enforcement, and health.

High-risk AI systems must meet comprehensive requirements under the EU AI Act including risk management systems, data governance measures, technical documentation, third-party conformity assessments, and post-market monitoring capabilities. Organizations must also prepare for DORA (Digital Operational Resilience Act) requirements, which became effective January 17, 2025, for all financial services entities.

DORA mandates comprehensive ICT third-party risk management frameworks including strategic ICT third-party risk strategies approved by senior management, vendor selection standards aligned to international security organizations, business continuity requirements for critical functions, mandatory contract provisions with specific security and audit clauses, and incident reporting arrangements.

To protect your organization from AI-related vendor risks, start with these key actions:

• Include AI-Specific Terms in Vendor Contracts: Make transparency, audit access, and breach notification non-negotiable.

• Require Algorithm Documentation: For critical services, ask for independent algorithm audits or detailed model cards. Don't settle for black-box answers.

• Test Vendor Security Before Launch: Run red-team exercises on exposed endpoints.

• Create a Unified Dashboard: Bring everything together in a unified third-party risk dashboard so your security, legal, and procurement teams stay aligned with a single source of truth.

Vendor questionnaires provide a snapshot of risk while attackers operate in real time. Annual reviews often struggle to detect active threats, making continuous control monitoring (CCM) essential. By gathering telemetry from vendor systems, CCM builds behavioral baselines and flags anomalies in real time. AI and automation can help organizations improve breach detection and containment times.

Transitioning to a mature TPRM program requires more than technology implementation. According to NIST SP 800-161, successful continuous monitoring depends on high-quality, structured vendor data and well-defined processes.

Data quality challenges, including incomplete logs and disorganized inputs, directly undermine the accuracy of AI-powered scoring algorithms and behavioral baselines.

To succeed:

• Stream normalized vendor logs into your SIEM and monitor activity across all touchpoints.

• Generate automated risk scores using behavioral signals.

• Establish per-vendor baselines and trigger auto-containment when deviations occur.

• Track metrics like time between risk-score changes, vendor coverage rates, and time to containment.

Additionally, avoid pitfalls like generic survey responses, overlooking fourth-party risks, or skipping baseline setup. According to NIST CSF 2.0, the Govern (GV) Function controls GV.SC-03 specifically mandates integrating third-party risk management into enterprise risk management processes, establishing continuous feedback loops for risk posture updates rather than periodic reviews.

Certifications such as SOC 2 and ISO 27001 provide important baseline security validation, but they are point-in-time snapshots rather than continuous assurance. Best-practice frameworks recommend using certifications as foundational validation during vendor onboarding and establishing monitoring to detect emerging threats and behavioral anomalies between formal audits.

Behavior-based monitoring fills this blind spot by learning each vendor's typical patterns, including login geography, email cadence, and invoice formats, then flagging anomalies. Altered banking details, foreign API usage, or late-night data access often signal early compromise, even when they don't violate compliance checklists.

Abnormal's behavioral AI detects anomalies and suspicious vendor activity patterns, enabling security teams to isolate compromised vendors or revoke access before attackers can expand their foothold within the organization's supply chain.

To enable this capability:

• Deploy anomaly detection across vendor-facing systems.

• Blend static signals with dynamic behavior analytics.

• Feed these insights into your SIEM and incident response workflows to accelerate containment.

Certifications remain useful baseline controls, but they should no longer serve as the primary vendor risk management approach.

Attackers are now weaponizing AI for multimedia fraud, marking a significant escalation beyond traditional email-based threats. Video and voice deepfakes have emerged as a sophisticated threat to executive impersonation attacks. Finance workers have authorized large payments after participating in video calls impersonating a CFO, demonstrating attackers' ability to exploit multimedia fraud to bypass traditional verification workflows.

Synthetic identity fraud compounds the threat environment, with sophisticated fake identities established and nurtured over months or years before exploitation.

Organizations must implement layered defenses.

Multimedia Verification Protocols: Require out-of-band confirmation for high-value financial authorizations to prevent deepfake fraud and BEC attacks.

Enhanced Vendor Onboarding: Implement synthetic identity detection during new vendor verification, as synthetic identities often require months or years of cultivation before exploitation.

During our "Inbox Under Siege" webinar, Piotr Bazydlo, Head of Threat Intelligence at Abnormal, described how this attack unfolds: “You might have a person on the other side with whom you do business for weeks, months, if not years. And one day, that person asks you to update the payment details for the next invoice. Everything about the email looks like every other email you receive from the person, so the chances of you going ahead and updating that are pretty high.”

Continuous Relationship Monitoring: Monitor vendor relationships for behavioral changes that may indicate compromise.

Callback Verification: Establish secondary verification channels for payment changes and wire transfers using authentic contact information maintained separately from vendor communications.

Fourth-party risk (the vendors your vendors use) represents a critical blind spot that organizations must actively manage within traditional TPRM programs. Third-party risk management must extend beyond direct vendors to include these extended relationships. Regulators increasingly scrutinize cascading supply chain risks.

To manage fourth-party risk effectively, organizations should focus on several key areas.

Document Subprocessor Relationships: Require critical vendors to disclose and maintain documentation on their own third-party dependencies, with particular attention to fourth-party providers whose disruption could create cascading supply chain risks across your ecosystem.

Include Contract Provisions: Add clauses requiring notification when vendors engage new subprocessors for critical functions, with specific timelines for notification and security assessments before the subprocessor becomes operational.

Assess Concentration Risk: Identify where multiple vendors rely on the same fourth-party providers, recognizing that concentrated dependencies represent systemic risk to your entire vendor ecosystem.

Monitor Cascading Breaches: Track security incidents at third-party providers that could affect your vendor ecosystem, with incident response protocols that enable rapid notification and containment across dependent third parties.

AI transforms third-party risk management by analyzing vast behavioral signals beyond human capacity. Abnormal's behavioral AI establishes baselines for normal vendor activity patterns, flagging deviations in real time through unsupervised learning algorithms that do not require labeled training data of previous vendor compromises.

These systems identify novel attack patterns not seen in historical data by detecting statistical deviations from established baselines. Modern solutions combine live telemetry with historical patterns to catch subtle anomalies, while security orchestration and response automation (SOAR) can enable rapid incident response for detected compromises.

Advanced analytics platforms group similar vendor behaviors, score new threats, and surface the few relationships that require immediate attention.

Currently, both generative AI and agentic AI simulate attack scenarios, adjust risk scores, and can even trigger remediation workflows without waiting for human input. With proper governance, this automation delivers scalable, adaptive defenses that evolve alongside attacker tactics.

To get started:

• Pilot AI tools with high-risk vendors first, implementing risk-tiered vendor classification per NIST frameworks.

• Group suppliers by behavioral patterns and risk tiers.

• Use transparent, explainable AI models that support auditability and align with GDPR and EU AI Act interpretability requirements.

• Feed analyst feedback into detection systems for continuous improvement and real-time risk intelligence integration.

Maintain performance by tuning alerts monthly, retraining for model drift, and preserving human oversight for critical decisions.

AI-driven third-party risk management is no longer a future goal but an operational reality. Organizations that implement behavior-based monitoring can detect anomalies and reduce risk across their supply chains.

Platforms that leverage user and entity behavior analytics (UEBA) identify red flags like unauthorized data transfers, suspicious logins, and unexpected access changes, offering the oversight modern regulations demand.

Abnormal's behavioral detection engine builds a baseline of each vendor's communication patterns within days, flagging deviations that traditional tools overlook. When combined with automated response playbooks, this approach minimizes incident dwell time and redirects valuable security resources toward long-term resilience.

Adopting AI modernizes vendor risk management, turning it into a dynamic defense mechanism that evolves as quickly as today's threats. See firsthand how Abnormal redefines third-party risk management with continuous AI-powered protection by booking a personalized demo.