What are the most common Gen AI security failures identified in recent testing?

The most frequent failures include prompt injection leading to policy bypass, data exfiltration through tool-enabled agents, and cross-tenant leakage caused by misconfigured connectors in RAG pipelines. Teams also report insufficient input sanitization and missing audit trails, which complicate incident response.

Which controls are enterprises now demanding from Gen AI platform providers?

CISOs are insisting on customer-managed encryption keys, zero data retention by default, region-specific processing, and verifiable tenant isolation. They also require detailed audit logs, model cards tied to contractual commitments, and independent red-team validation of jailbreak defenses.

How do frameworks like OWASP and MITRE ATLAS help reduce Gen AI risk?

OWASP’s LLM Top 10 and MITRE ATLAS provide common language and attack patterns for engineers and defenders, enabling consistent threat modeling and testing. Organizations translate these patterns into policy-as-code, CI/CD guardrails, and canary prompts to detect degradation or new attack vectors.

What is changing in vendor transparency around privacy and training data?

Vendors are publishing trust documents clarifying data flows, retention policies, and whether customer inputs are used for model training. Enterprises increasingly require explicit contractual prohibitions on training with customer data and expect ongoing attestations rather than one-time certifications.

What should security leaders prioritize for Gen AI deployments over the next quarter?

Priorities include hardening RAG and agent toolchains, implementing prompt gateways with enforceable policy, and commissioning third-party red teams for multi-modal and tool-augmented scenarios. Leaders should also align procurement with NIST AI RMF, CISA secure-by-design principles, and require transparent vendor logging and isolation proofs.

Gen AI Vendors Scramble To Seal Data Leaks as Red-Team Findings Put Privacy on Notice

Security tests and compliance audits this month intensify pressure on Gen AI platforms to prove data isolation, rein in prompt injection, and prevent cross-tenant leakage. Enterprises are demanding audit-grade guarantees, customer-managed keys, and documented jailbreak defenses from OpenAI, Microsoft, Google, AWS, and Anthropic.

Published: November 28, 2025 By David Kim Category: Gen AI

Gen AI Vendors Scramble To Seal Data Leaks as Red-Team Findings Put Privacy on Notice

Confidential Data At Risk, Enterprises Push for Proof

A fresh wave of enterprise red-team exercises and compliance checks this month is exposing how vulnerable generative AI deployments remain to prompt injection, data exfiltration, and cross-tenant leakage. Security teams probing retrieval-augmented generation (RAG) workflows report that misconfigured connectors and insufficient guardrails can still coax models to reveal snippets of sensitive content when adversarial prompts are chained together. These findings are amplifying calls for verifiable data isolation and stricter human-in-the-loop policies before production rollouts scale further.

Major platforms are in the spotlight. For more on related agentic ai developments. Buyers say they want explicit controls around training data retention, tenant boundaries, and export logging across OpenAI, Microsoft, Google Cloud, Amazon Web Services and Anthropic, with increasing emphasis on customer-managed encryption keys and geo-fenced storage. Security architects are aligning playbooks to the OWASP Top 10 for LLM Applications and MITRE ATLAS to design model-facing services that assume jailbreak attempts and supply-chain compromise as baseline threats.

Governance Tightens: SOC 2, HIPAA, and Regional Isolation

As procurement cycles close, buyers say they are tying spend to audit-grade attestations. That includes SOC 2 Type II, HIPAA Business Associate Agreements (BAAs) for clinical use cases, and region-specific processing for regulated workloads in the EU and APAC. CIOs evaluating copilots and AI assistants from Microsoft and Google Cloud describe contract riders that require zero data retention for prompts and responses unless explicitly enabled, plus hard guarantees that customer inputs are never used to train foundation models.

Data localization and private networking are moving from nice-to-have to mandatory. Enterprises are pushing AWS and Google Cloud to document egress paths, break-glass procedures, and key-rotation schedules for customer-managed keys (CMK), while demanding reproducible red-team evidence from OpenAI and Anthropic that jailbreak mitigation layers stand up to chained and multi-modal attacks. This builds on broader Gen AI trends...

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