Bedrock Data, a leading provider in DSPM, data security, and governance, has introduced two major innovations Bedrock Data ArgusAI and Natural Language Policy designed to transform how enterprises manage and govern artificial intelligence. With this launch, the company moves deeper into AI governance, offering organizations the visibility and control they need as AI systems become more deeply integrated across cloud and on-premises environments.
ArgusAI marks a significant step forward by helping enterprises understand precisely what data their AI models and agents access during both training and inference. Moreover, it evaluates whether existing guardrails are strong enough to prevent sensitive data exposure. Meanwhile, Natural Language Policy enables teams to define and implement data controls using simple English, making governance more accessible and consistent across systems. When used together, these capabilities empower businesses to govern their AI environments with the same ease and precision they apply to traditional data systems.
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As AI adoption accelerates, governance has emerged as the new bottleneck. While organizations can deploy AI faster than ever, existing tools often fail to validate guardrails or enforce policies effectively at enterprise scale. Bedrock addresses this critical gap by tapping into its petabyte-scale Metadata Lake, giving ArgusAI the context and automation needed to bridge the divide between data governance and AI governance.
To help enterprises overcome risks tied to opaque data usage and inconsistent controls especially when building on services like Amazon Bedrock ArgusAI introduces two powerful capabilities. First, its AI Data Bill of Materials (DBOM) automatically maps datasets used in training and inference to Amazon Bedrock Custom Models, RAG-based Knowledge Bases, and Agents. Second, its Guardrail Gap Analysis and Remediation capability tests whether technical guardrails are strong enough to prevent sensitive data leakage and provides precise remediation steps for security, product, and ML engineering teams.
Additionally, ArgusAI leverages Bedrock Data’s expansive Metadata Lake to compare policy requirements with actual model exposure. For instance, if a policy states that AI agents must not process or reveal credit-card data, the platform identifies where that data appears in the training or inference pipeline, checks whether guardrails work as intended and automatically recommends how to suppress or mask the data.
Further strengthening this ecosystem, Bedrock’s Natural Language Policy and Investigation feature allows legal, GRC, and security teams to articulate governance requirements in plain English. Statements like “Only members of HR may view employment data” or “EU personal data must remain within EU regions” are instantly translated into system-wide enforceable controls. Combined with ArgusAI, this capability extends AI governance into a unified operational language across data, identity, and AI. Teams can quickly ask questions such as “Which models can access PHI data?” or “Which identities interacted with customer PII through an agent?” and receive precise, evidence-backed answers based on data lineage and access logs.
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Highlighting the urgency of AI governance, Pranava Adduri, CTO and co-founder of Bedrock Data, said, “Enterprises face a dual challenge: accelerating AI adoption while ensuring governance keeps pace. The fundamental problem is that policies can’t be enforced across fragmented systems that each speak different control languages. When business teams ship AI quickly and governance can’t keep up due to system complexity, the gap between policy intent and enforcement becomes unacceptable. By tying every model to its DBOM, assessing and fixing guardrail deficiencies and translating natural language into enforceable controls, ArgusAI makes responsible AI operational at scale.”
Bedrock Data confirmed that ArgusAI along with its DBOM, Guardrail Gap Analysis, Remediation, and Natural Language Policy and Investigation features is available now within the Bedrock Data Platform. With these capabilities, enterprises can confidently scale AI adoption while ensuring every system remains compliant, transparent, and secure.
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