How Billtrust Turns Data Governance Into a Foundation for Responsible AI

As enterprises accelerate their adoption of artificial intelligence (AI), many find themselves navigating a paradox: how to innovate fast enough to stay competitive without compromising ethics, privacy, or regulatory compliance.

For Billtrust, a leader in accounts receivable (AR) and payment automation, this challenge isn’t a paradox at all—it’s a matter of applying discipline already proven to work.

“AI doesn’t get special treatment,” said Ankur Ahuja, Billtrust’s Chief Information and Security Officer (CISO). “It’s the same strong, audited controls that protect all our financial data. If you follow strong data security policy, it automatically replicates to strong AI security policy.”

In other words, responsible AI starts with data governance, not a new rulebook.

Overview: Billtrust’s Responsible AI Framework

Key Focus Area	Billtrust’s Approach
Data Governance	Unified security standards for all data (financial, operational, or AI-generated)
Regulatory Compliance	Adherence to PCI, SOC 1/SOC 2, GDPR, and CCPA
Vendor Accountability	Verification of partner compliance and data-use transparency
Operational Resilience	AI included in disaster recovery and backup plans
Human Oversight	“Human-in-the-loop” policy for all financial decisions

This integrated framework positions AI as an extension of existing data systems—not a standalone risk. “Data is data,” Ahuja noted. “Secure data, control access, ensure compliance—the same principles apply.”

Security as a Product Feature

For Billtrust, security is not a back-office function but a core product differentiator. As more of its AR and payments processes become AI-assisted, the company’s credibility depends not only on its algorithms but also on how securely those algorithms handle sensitive financial data.

“Our approach to AI governance is basically enabling it—but with guardrails,” Ahuja explained. “We want teams to innovate with AI, but in a way that protects customer trust, data integrity, and intellectual property.”

That mindset reflects a broader shift among enterprise CISOs: seeing security as a value proposition, not a limitation.

“Whether it’s retrieval-augmented generation, vector databases, or anonymization, everything must align with how you treat your current data,” Ahuja said. “AI doesn’t get a pass.”

Extending Governance Principles to AI Systems

Billtrust treats AI data the same way it treats payment data—with segmentation, encryption, and continuous monitoring.

• PCI Controls: All payment-related data is encrypted, monitored, and stored in segmented networks.
• SOC 1 & SOC 2 Compliance: Logical access, exchange management, and data handling are governed by strict border security protocols.
• GDPR & CCPA Compliance: Customer data used in AI systems is handled under the same consent, deletion, and privacy policies as other datasets.

“We ensure customer data is only used for the purpose it was collected, with clear consent and deletion rights,” said Ahuja.

This consistency simplifies compliance and reduces confusion about what qualifies as “AI data.”

The Vendor Accountability Playbook

Ahuja emphasizes that responsible AI isn’t limited to internal systems—it must extend to every external partner in the ecosystem.

“Do you use customer AR data in training foundational public models?” Ahuja asked. “Expect a clear ‘no’ answer. If they say yes, then there’s something you need to dig into.”

At Billtrust, vendor selection follows a trust-but-verify principle:

• Vendors must disclose how they process, store, and secure AI-related data.
• External systems must prevent data leakage in both inputs and outputs.
• AI partners must maintain audit trails for all data interactions.

“These are the kinds of questions every CFO should ask,” Ahuja said. “Make sure their data is secured wherever it is, whichever vendor is taking care of it.”

Embedding AI Into Enterprise Resilience

Billtrust treats AI as part of its business continuity plan. Backup and disaster recovery policies extend not only to physical infrastructure but also to AI models and training datasets.

“We maintain backups and disaster recovery plans not only for our infrastructure, but also for AI models,” Ahuja explained.

This approach reduces downtime risk and ensures that even if an AI system fails, critical operations continue uninterrupted.

Continuity Measure	Coverage
Disaster Recovery	Infrastructure + AI models
Data Backups	Training datasets, configurations, and audit logs
Fail-Safe Operations	Human review for all financial outputs
Validation Layers	Multi-tiered model testing and drift detection

Confidence Through Consistency

While new AI risks such as hallucinations and model drift pose operational challenges, Billtrust counters them through layered validation and human oversight.

“We don’t let machines make financial decisions,” Ahuja said. “Humans are involved when it comes to collections or credit-risk evaluations.”

This “human-in-the-loop” model ensures that accountability remains clear—and trust remains intact.

By grounding AI deployment in long-standing governance principles, Ahuja believes organizations can turn compliance from a burden into a catalyst for innovation.

“It’s about awareness and enablement,” Ahuja said. “Make sure the entire company knows how AI data is processed and protected.”

Expert Perspectives: Building Responsible AI Through Governance

1. Ankur Ahuja, CISO, Billtrust

“AI doesn’t need special treatment. Strong data security naturally leads to strong AI security.”

2. Dr. Karen Meyers, AI Governance Analyst

“Billtrust’s model illustrates that responsible AI isn’t about new rules—it’s about applying proven governance at scale.”

3. Rajiv Patel, Enterprise Risk Advisor

“Including AI models in disaster recovery planning is a next-generation best practice. It’s the mark of a mature security culture.”

4. Emily Torres, Data Privacy Expert

“Treating AI under the same consent and privacy frameworks as other data sets a critical precedent for ethical compliance.”

Why This Matters?

As AI becomes embedded in financial, healthcare, and enterprise systems, the line between AI governance and data governance continues to blur. Billtrust’s approach offers a blueprint: unify, don’t complicate.

By integrating AI within existing security, privacy, and compliance frameworks, organizations can scale innovation responsibly — ensuring that trust remains the foundation of progress.

Responsible AI, as Ahuja puts it, doesn’t mean slowing down. It means building on what already works.

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