The Challenge: Why Agentic AI projects fail

Enterprises and regulated industries face unique challenges deploying agentic AI at scale, because autonomous agents interact with highly sensitive data. To dive into this deeper, let's look at what those autonomous capabilities are and how they pose a security challenge.

Agent capabilities: 

• They can access and integrate data from multiple systems, external APIs and vector stores. 
• They act autonomously across those systems. 
• They can also interact with external tools, MCP servers or even other agents that act as tools. 
• Finally, they can also generate content that may include sensitive data and spread it easily to those external tools, agents. 

Emerging problems: 

This autonomy and broad access creates critical gaps for enterprises and regulated industries. 

• Security: Current security measures around document-level access controls fail to protect the sensitive content within documents.
• Identity management: Archaic user identity management that cannot enforce field-level access controls or pass user identity and attributes through every layer of the stack. 
• Risk and compliance: Lack of real-time data protections or access scoped to agent-level with end-to-end auditability and governance. 

Bottom line: 

The need to leverage AI to improve efficiency and speed is most critical for regulated industries and enterprises, but as a result of these emerging challenges, AI initiatives are stuck in a proof of concept (POC) purgatory forever, preventing these organizations from realizing their full potential and ROI.

While AWS delivers a robust, secure infrastructure with strong access controls, enterprises will benefit from enhanced runtime data protections, scoped agent-level access, and end-to-end governance to safeguard sensitive PII, PHI, PCI, and financial data throughout AI workflows. 

Skyflow’s Runtime AI Data Security and the Data Control Layer

Skyflow Runtime AI Data Security solution addresses this by providing the dedicated Data Control Layer, delivering runtime, field-level, and identity-aware protections with additional visibility and enforceable policies that ensure data remains protected at every step, complementing AWS’s capabilities for regulated and enterprise environments.

Key Points on AWS integration:

• With AWS Quick Suite, Skyflow enforces runtime sensitive data protection, inspecting and de-identifying PII/PHI before it reaches any agent or model, while ensuring compliance with regulations like GDPR and HIPAA, as data flows across systems.‍
• AgentCore Security: For AWS AgentCore orchestration, Skyflow implements Agent identity binding and enforces policy-based data access at runtime. This ensures that every component, especially during AgentCore's orchestration of agent behavior (runtime, memory, tools, actions), only interacts with the "minimum necessary" data, and provides field-level logging for complete auditability.

This delivers an end-to-end data flow security that is real-time, field-level, identity-aware, guaranteeing security wherever the data resides or moves within the architecture.

Key capabilities of the Data Control Layer:  

1. Runtime Sensitive Data Protection

This capability ensures that sensitive data is protected at the moment it is being used by the AI system:

• Inspection and De-identification: As Quick Suite applications retrieve information or as AgentCore orchestrates agent behavior (including tools, memory, and API calls), Skyflow inspects, de-identifies, and anonymizes sensitive data (PII/PHI/PCI) before any agent or model sees it.
• Data Minimization: This process ensures that the AI preserves full referential context while guaranteeing raw sensitive data never enters prompts, agent memory, tool outputs, or logs.
• Policy Enforcement: Policies automatically enforce “minimum necessary” access so that Quick Suite applications and AgentCore agents operate within the scopes permitted by enterprise rules.
• Output Scanning: Outputs generated by the agents are scanned before they are presented to the user or indexed into a database to ensure adherence to access policies.
• Customer Control: Enterprises maintain complete control over what sensitive data enters any AI workflow, whether orchestrated through Quick Suite or executed by AgentCore agents and tools.

2. Agent Identity and Unified Privacy Controls

The Data Control Layer ensures that security policies are enforced based on the identity and context of the agents and the user they are acting on behalf of:

• Agent Identity Binding: Skyflow ensures accountability by giving each agent, tool, and sub-agent a cryptographically verifiable identity and a contextual access token tied to the end user.
• Policy-Based Access at Runtime: Skyflow evaluates requests in real-time, using both user identity (supplied by AgentCore) and agent identity to determine dynamically whether sensitive data should be de-identified, masked, or restricted. This is known as Just-in-Time Authorization.
• Context-Aware Re-identification: Data can be re-identified only if the requesting agent is authorized during inference or tool execution, preventing oversharing or leakage.

3. Cross-System and Regional Data Boundaries

This aspect focuses on governance and compliance as data moves within and outside the AWS environment:

• Fine-Grained Governance: Skyflow enforces consistent, fine-grained governance on sensitive information (PCI, PII, PHI), even within documents, as AI applications or agents access data across systems, regions, tools, APIs, and data stores.
• Regional Compliance: It ensures region-aware compliance (GDPR, HIPAA, CCPA, local data residency) as data flows through Quick Suite workflows or AgentCore tools and memories.
• Dynamic Transformations: Skyflow applies policy-driven pseudonymization, masking, and access restrictions dynamically as data moves between agents, external APIs, enterprise databases, SaaS platforms, and vector stores. This is crucial for maintaining regulatory compliance while scaling AI workflows.

4. End-to-End Auditability

Finally, the Data Control Layer provides the necessary oversight for compliance and risk mitigation:

• Field-Level Logging: Skyflow delivers deep, comprehensive field-level logging of all sensitive data interactions, transformations, and access.
• Verifiable Record: This logging creates a verifiable record of data usage, which is critical for meeting strict regulatory requirements, mitigating risk, and generating audit reports for regulators

In summary, the Data Control Layer acts like a secure guard that stands between the autonomous AI agents and the sensitive corporate data. It doesn’t stop the flow of data but, in real-time, inspects the contents carefully, checks for identity, and dynamically applies policies to ensure only protected usable data proceeds, quickly and securely, while creating a perfect, time-stamped log of every action taken. 

Value Proposition

To recap, the core value proposition, “the why” the solution described here is necessary, stems from the conflict between the capabilities of autonomous AI agents that we explained in the beginning and the strict requirements for data security and governance, especially in regulated industries. 

As agents accelerate innovation, they interact with highly sensitive and regulated information and the critical lack of adequate protection often causes AI initiatives to stall. 

Together, Skyflow + AWS Quick Suite & AgentCore enables enterprises to now securely and confidently deploy agentic AI at scale across healthcare, finance, retail, and other regulated workflows ensuring they can realize the ROI and continue to innovate. 

Customer Use Case 

To bring this combined solution to life, here are the details of a Healthcare customer and their use case for AI adoption, the challenges they faced and how they solved them with Skyflow and AWS.

Enabling Secure Agentic AI in Healthcare 

The customer is a large U.S. healthcare provider with multiple hospitals and research centers.

Goal of AI Adoption: The customer aimed to accelerate AI adoption across their clinical workflows. They intended to use agentic AI to perform tasks such as generating patient summaries, analyzing medical imaging, and surfacing insights from electronic health records (EHRs)

AWS Role: AWS Quick Suite and AgentCore provided the necessary enterprise-grade LLMs, agent orchestration, and workflow automation. It enabled the integration of data across multiple hospital systems, research databases, and analytics tools, ensuring scalable AI operations and seamless integration with EHRs and imaging systems

The Challenge:
Healthcare data is highly sensitive and regulated. While Quick Suite provides secure infrastructure, access controls, and workflow governance; the customer, to ensure compliance with HIPAA and other regulations when AI agents operate on PHI, required 

• Fine-grained runtime protections 
• Scoped agent-level access 
• End-to-end auditability 

The Skyflow+AWS Solution:

Here is how the healthcare company was able to solve their challenges with the combined solution from Skyflow and AWS.

Here are some high-level diagrams to visualize the integration. 

Skyflow Runtime AI Data Security + AWS Quick Suite 

Skyflow Runtime AI Data Security + AWS AgentCore  

The Outcome:

By combining AWS Quick Suite and AgentCore's orchestration with Skyflow's Data Control Layer, the healthcare provider achieved the following results:

• The customer scaled agentic AI workflows beyond pilot projects into production.
• They were able to maintain patient privacy, regulatory compliance (HIPAA), and enterprise governance.
• Physicians and researchers could safely access AI-generated insights, leading to improved operational efficiency and clinical decision support.
• In essence, the Data Control Layer provides the necessary security assurance to confidently move sensitive healthcare AI applications from experimental stages to full operational deployment

Reach out to us to learn how your organization can leverage Skyflow’s Runtime AI Data Security solution and AWS's robust security foundation to realize the value of autonomous, agentic AI.