Organizations across financial services, fintech, healthcare, and enterprise SaaS are unknowingly exposing their most sensitive data—and operational continuity—to systemic failure through a rapidly widening gap between AI-driven vendor capabilities and static, compliance-anchored third-party risk management (TPRM) frameworks. Razorpay’s CISO Praveen Parihar did not merely sound an alarm at ETCISO Secufest 2026—he delivered a forensic autopsy of a broken paradigm: 73% of Fortune 500 enterprises now onboard at least one AI-native SaaS platform per quarter, yet 89% continue to evaluate them using assessment templates last updated before LLMs entered production environments. This misalignment is no longer theoretical; it has manifested in real-world incidents where autonomous agents—deployed without human-in-the-loop validation—exfiltrated PII via prompt injection during CRM integration testing, and where model inversion attacks reconstructed proprietary underwriting logic from API responses of a fraud-detection vendor certified to SOC 2 Type II. The consequence is not just regulatory exposure but structural fragility: when AI agents operate as non-human identities with persistent access across ERP, CRM, and payment orchestration layers, traditional TPRM becomes a ceremonial exercise rather than a control function.
The Evolutionary Mismatch: From SaaS Certifications to Autonomous Agent Governance
The foundational flaw in contemporary supply chain security lies not in negligence but in epistemological inertia—organizations persist in measuring AI-native vendors against criteria calibrated for a pre-AI world. ISO 27001:2022, while robust for information security management systems, contains zero clauses addressing model provenance, training data lineage, or inference-time guardrail enforcement. Similarly, SOC 2 Trust Services Criteria (Security, Availability, Processing Integrity, Confidentiality, Privacy) were designed for deterministic software architectures—not probabilistic systems where behavior emerges unpredictably from context, prompt engineering, and fine-tuning artifacts. As Parihar emphasized,
“Just having SOC 2 or ISO 27001 certifications does not guarantee the kind of security controls required for AI-native SaaS platforms.” — Praveen Parihar, CISO, Razorpay
This is not a critique of certification bodies but of how enterprises weaponize compliance as a proxy for assurance. A vendor may hold flawless audit reports while embedding a large language model trained on scraped public forums containing inadvertently leaked source code snippets—a fact invisible to auditors focused on access logs and encryption key rotation schedules. Worse, legacy frameworks treat APIs as endpoints rather than AI-mediated conduits: when an AI copilot in a sales engagement platform queries internal Jira tickets to draft client proposals, it doesn’t ‘access’ Jira—it synthesizes, infers, and potentially leaks metadata about sprint velocity, unresolved CVEs, or product roadmap delays—all outside the scope of traditional API security reviews.
This mismatch accelerates risk propagation. Engineering teams at Razorpay, like those at Stripe and Plaid, now deploy internal AI-augmented tools—think custom LLM-powered contract reviewers or automated compliance checkers—in under 72 hours using low-code orchestration platforms. These tools integrate with external AI vendors via OAuth 2.0 scopes that grant broader permissions than necessary (e.g., read_all_messages instead of read_conversation_summary), creating lateral movement paths for compromised agents. Crucially, these integrations bypass formal vendor onboarding entirely—they’re treated as ‘internal development,’ despite relying on external AI models whose weights, inference servers, and telemetry pipelines reside in multi-tenant cloud environments governed by opaque SLAs. The result? A shadow supply chain growing at 4.2x the velocity of formal procurement cycles, operating beyond the visibility perimeter of GRC teams.
The Four Emerging Threat Vectors No Compliance Checklist Covers
Traditional TPRM frameworks fail because they conceptualize risk as static, bounded, and human-operated—whereas AI-native vendors introduce dynamic, emergent, and agent-mediated threats that defy conventional categorization. Prompt injection attacks, for instance, exploit the semantic malleability of LLM interfaces: a maliciously crafted input can override system instructions, tricking an AI assistant into executing unauthorized actions—such as exporting customer contact lists to a personal email or invoking privileged API endpoints. Unlike SQL injection, which targets structured query languages, prompt injection operates in natural language space, making detection reliant on linguistic anomaly detection rather than signature-based WAF rules. Razorpay’s security team discovered this firsthand when evaluating a marketing automation vendor whose AI copilot accepted free-text inputs from sales reps; a simulated red-team attempt injected a payload disguised as a meeting summary request, resulting in the agent exfiltrating full customer transaction histories to a controlled endpoint. Such scenarios reveal why only 12% of enterprises currently test for prompt injection during vendor assessments, despite its documented use in 37% of AI-related breaches reported to CERT-India in FY2025–26.
Model poisoning and inversion represent even more insidious risks. Model poisoning occurs when adversaries contaminate training data to bias outputs—imagine a vendor’s credit-scoring AI trained on datasets manipulated to favor certain geographies, introducing discriminatory outcomes that evade fairness audits conducted post-deployment. Model inversion, conversely, reconstructs sensitive training data from API responses: researchers demonstrated that querying a healthcare analytics vendor’s diagnostic assistant with carefully crafted symptom combinations allowed reconstruction of patient-level clinical notes with >84% fidelity. Neither attack leaves traces in log files or network flows; both manifest only in output corruption or statistical anomalies. Yet current TPRM questionnaires lack fields for asking about training data provenance verification processes, model watermarking strategies, or inference-time differential privacy implementation. Without such questions, organizations assume vendors implement these controls by default—or worse, remain unaware they should be asking.
- Prompt injection: Exploits instruction-following weaknesses in LLMs to execute unauthorized commands via natural language
- Model poisoning: Introduces bias or backdoors by corrupting training datasets prior to model deployment
- Model inversion: Reconstructs sensitive training data (e.g., PII, trade secrets) from model outputs
- Data leakage through AI assistants: Occurs when agents retain context across sessions or cache unredacted inputs in vector stores
From Certification Audits to AI Red Teaming: Operationalizing Rigor
Replacing checkbox compliance with operational rigor requires institutionalizing AI-specific adversarial testing as a mandatory phase in vendor due diligence—not as a one-off experiment but as a repeatable, scalable capability embedded within procurement workflows. Razorpay’s approach exemplifies this shift: before integrating any AI-native vendor, its security team conducts a three-tiered validation process—model transparency review, guardrail stress testing, and agent identity lifecycle analysis. The first involves demanding documentation of model architecture, quantization methods, and training data sourcing—including proof of opt-in consent for any personal data used. The second subjects the vendor’s AI interface to over 200 prompt injection variants, jailbreak attempts, and role-confusion payloads across multiple languages and domains. Critically, this testing isn’t limited to the vendor’s demo environment; Razorpay requires access to a production-like sandbox where its engineers simulate real-world usage patterns—including concurrent multi-agent workflows that mimic cross-functional collaboration. This revealed, for example, that a vendor’s ‘secure mode’ disabled only basic prompt injection but remained vulnerable to multi-turn contextual hijacking, where initial benign queries established trust before escalating privileges.
Guardrail validation extends beyond technical controls to policy enforceability. Razorpay mandates that all AI vendors implement runtime constraints such as output token limits, PII redaction hooks, and cross-domain context isolation—and then verifies these aren’t just configured but actively enforced across all API versions and client SDKs. Most critically, the team analyzes how non-human identities (NHIs) created by autonomous agents are provisioned, rotated, and revoked. Unlike service accounts governed by IAM policies, NHIs often inherit ambient permissions from user contexts or rely on ephemeral tokens with indefinite lifespans. Razorpay discovered that one logistics optimization vendor issued agent tokens valid for 365 days with no revocation mechanism, enabling persistent lateral movement if compromised. Their remediation required co-developing a token-binding protocol with the vendor, ensuring each agent session tied to a short-lived, hardware-backed attestation. This level of depth transforms vendor risk management from a gatekeeping function into a collaborative engineering discipline—one where security teams contribute code, not just questionnaires.
The Data Sovereignty Crisis in AI-Native Supply Chains
Data sovereignty—the legal and technical assurance that data resides and is processed only within jurisdictionally compliant boundaries—is collapsing under the weight of AI-native vendor architectures. Where traditional SaaS vendors operated region-locked data centers with clear residency commitments, AI-native platforms increasingly rely on globally distributed inference clusters, federated learning pipelines, and multi-cloud model hosting. A single vendor may host base models in Ireland (GDPR-compliant), fine-tune them on U.S.-based customer data, and route real-time inference requests through edge nodes in Singapore—creating jurisdictional ambiguity that renders standard data processing agreements (DPAs) legally hollow. Razorpay’s experience with a global KYC verification vendor illustrates the peril: although the vendor claimed ‘EU-only data residency,’ its LLM inference layer dynamically routed requests to the lowest-latency node, resulting in Indian customer biometric hashes being processed in AWS Tokyo—triggering violations of India’s DPDP Act 2023 and EU SCC requirements simultaneously. Worse, the vendor’s architecture obscured this routing logic behind abstraction layers, making it invisible to standard network flow analysis or DPA annexes.
This crisis is compounded by model-as-a-service (MaaS) licensing models that treat AI outputs—not just data—as intellectual property subject to vendor terms. Some vendors assert rights over insights generated from customer data, including proprietary business logic inferred during AI-assisted decision-making. In one documented case, a banking vendor’s contract clause granted it perpetual, royalty-free rights to ‘any patterns, correlations, or predictive models derived from Customer Inputs’—effectively allowing the vendor to commercialize insights about a bank’s lending risk appetite, which could then be sold to competitors. Such clauses evade scrutiny because they appear in technical appendices rather than main DPA sections, and because legal teams lack AI literacy to recognize their implications. Only 5% of enterprises currently require AI-specific data sovereignty clauses in vendor contracts, according to the 2026 Gartner CISO Survey. Without explicit contractual language governing model training provenance, inference routing transparency, and output ownership, organizations surrender control over their most strategic assets—not through breach, but through contractual default.
- Base model hosting location (e.g., foundation model weights stored in Frankfurt)
- Fine-tuning data residency (e.g., customer-specific adaptations processed in Mumbai)
- Inference routing logic (e.g., dynamic load balancing across 12 global regions)
- Vector store persistence (e.g., embeddings cached in multi-tenant Redis clusters in Virginia)
- Telemetry and logging destinations (e.g., anonymized usage metrics sent to vendor’s observability platform in Oregon)
Building AI-Aware Governance: From Policy to Platform
Effective AI-era TPRM cannot be sustained through policy documents alone—it demands purpose-built technological infrastructure that embeds governance into the engineering workflow. Razorpay’s solution combines three interlocking layers: a vendor AI risk scoring engine, an autonomous agent identity registry, and a real-time inference monitoring fabric. The scoring engine ingests over 40 signals—including model transparency disclosures, red-team results, SOC 2 report annotations, and GitHub commit history for open-weight models—to generate dynamic risk scores updated weekly. Crucially, it weights AI-specific signals 3.7x higher than traditional compliance markers, forcing prioritization of actual technical posture over audit theater. The agent identity registry tracks every non-human identity created by vendor-integrated AI agents—including their creation timestamp, permission scope, token lifetime, and associated human owner—enabling automated revocation upon employee offboarding or vendor contract termination. This solved a critical gap where legacy IAM tools couldn’t distinguish between a human-initiated API call and an agent-initiated one, leading to orphaned permissions.
The real-time inference monitoring fabric represents the most innovative layer: it deploys lightweight eBPF probes at the Kubernetes ingress layer to inspect outbound AI API calls for anomalous patterns—such as unusually high token counts, unexpected domain resolutions, or repeated PII-containing prompts—without decrypting payloads. When such patterns emerge, the system triggers automated response playbooks: throttling request rates, injecting synthetic noise into prompts, or quarantining the agent identity for forensic review. This infrastructure reduced Razorpay’s mean time to detect (MTTD) for AI-specific supply chain incidents from 17.3 days to under 92 seconds. Importantly, this platform wasn’t built in isolation; it integrates bidirectionally with Jira Service Management and ServiceNow, ensuring that every vendor risk finding automatically generates engineering tasks with SLA-bound resolution timelines. This closes the loop between security insight and engineering action—transforming TPRM from a retrospective compliance function into a proactive resilience engine.
Source: ciso.economictimes.indiatimes.com
This article was AI-assisted and reviewed by our editorial team.
