AI Factory

Why AI Factories Matter

AI factories are becoming the operating system for enterprise AI. They turn pilots into real outcomes, but only when they are secure.

Standardization

Reusable patterns that let teams scale new AI initiatives without rebuilding foundations.

Business-Aligned

Teams focus on use cases rather than infrastructure, speeding up delivery and value creation.

Agility

Faster iteration and deployment supported by purpose-built AI infrastructure.

Cost Efficiency

Optimized resources and workflows designed specifically for AI workloads.

Why Traditional Governance Isn’t Enough

Connected assistants supercharge dev velocity, but they also expand your attack surface. Developers may unknowingly transmit sensitive code, exceed RBAC boundaries, or trigger compliance violations. Autonomous agents add operational risk when unsupervised actions touch critical systems.

Secrets & IP leaks to external models

Data overreach beyond role/project context

Autonomous agents making unsafe changes

Pebblo Answer

MCP-Native Security

Govern data and tool access at the protocal level. Pebblo MCP validates permissions and sanitizes payloads before they ever reach your AI assistants

Agent Behavior Controls

Policy based guardrails prevent unsafe autonomous actions and contain misbehaving agents before they impact systems or codebases

Data Loss Prevention

Real-time inspection blocks secrets, credentials and proprietary code from leaving your environment, without slowing developers down

Injection & Supply-Chain Defense

Detect prompt/code injection patterns and vet third party MCP servers to reduce supply chain risk in your dev tool stack

Why AI Factories Must Be Secure

AI factories bring together data, models, and autonomous agents. Without the right controls, they create openings that attackers can exploit.

Prompt Injection

Models and agents can be steered off-policy through crafted inputs, leading to data exposure or unintended actions.

Agent Compromise

Over-permissioned agents can be manipulated into deleting data, triggering workflows, or moving across systems without oversight.

Data & Model Poisoning

Manipulated data entering ingestion, retrieval, or training pipelines can quietly corrupt model behavior and trust.

Inference & Extraction Attacks

Attackers target serving layers to replicate model logic, extract embeddings, or exploit vulnerabilities at runtime.

AI Firewalls Fail When AI Touches Real Data

AI firewalls try to filter model output after the fact without knowing what data went in. With no access controls, lineage, or compliance context, they end up guessing what a user shouldn’t see. That’s not governance. It’s guesswork.

Daxa’s TwinGuard architecture flips this model. SafeConnectors pull fine-grained permissions from your enterprise systems, and SafeRetriever applies them before any data reaches the LLM. Only authorized, compliant context moves forward—giving enterprises the confidence to run GenAI securely in real production.

Filtering Governance

No oversharing

// Security

Three Layers of AI Factory Security

Daxa secures the full AI pipeline so data, models, and agents operate safely at scale.

Secure Data

Fine-grained access controls and compliant retrieval before data reaches the model.

Secure Models

Protection against poisoning, unsafe context, and inference-level exploitation

Secure Usage

Guardrails that control how agents act, what they can access, and how AI is used across the enterprise.

DAXA: The AI Data Security Platform

Securely connect AI apps and agents to the data they need.

Deep Data Visibility

Track every data source, ingestion path, and retrieval with AI BOMs, AI-SPM, and full auditability across apps and agents.

Access Control

Enforce who can retrieve what with precise identity and policy checks before any context reaches the LLM.

Continuous Governance

Apply regulatory and internal policies automatically with real-time monitoring and audit readiness.

Threat Prevention

Stop data theft, prompt injection, poisoning, and adversarial inputs before they impact your AI systems.

MCP Trust Zone

Data-aware guardian agents secure MCP connections and prevent compromised agents from touching sensitive data.

Safe Agent Framework

Run autonomous agents with built-in guardrails that keep actions and data access within authorized boundaries.

// Proven outcomes

Financial Services - Trading Platform Development

Protected proprietary trading algorithms from model exposure while keeping Cursor-based assistance for non-sensitive code. AI velocity maintained; IP safeguarded.

0 source leaks

Full AI audit trail

No workflow changes

Healthcare Technology-HIPAA-Compliant Development

PHI never reaches external models. Teams use Copilot for general development while Pebblo enforces HIPAA-aligned policies and auditability.

PHI redaction on

HIPAA Controls

Faster releases

Enterprise Software - Global Dev Teams

Unified policy across geos, tools, and the SDLC. Consistent governance for Cursor, Copilot, and MCP-connected systems at global scale.

1 policy plane

Global coverage

Minutes to onboard

// USe Cases

AI Factory Security Use Cases

Real-world scenarios where Daxa secures data, agents, and workflows across the AI lifecycle.

Secure Coding Assistants

Deploy on-prem with full IP control

Restrict access to internal code and docs

Prevent code leaks and unauthorized retrieval

Support custom models fine-tuned on proprietary code

Audit all developer-AI interactions

Protected Agent Workflows

Secure CrewAI, n8n, and multi-agent deployments

MCP Trust Zone prevents lateral movement

Enforce reasoning-driven data/tool access

Full visibility into agent activity

Compliant AI Development

Generate AI Data BOMs automatically

Track data lineage and interactions

Meet GDPR, HIPAA, PCI-DSS

Enforce policies at scale

Zero Trust AI Access

Identity-based data authorization

Role-specific context retrieval

Prevent insider threats and overexposure

Enforce fine-grained permissions

AI Red Teaming

Run adversarial tests before production

Identify and mitigate security gaps

Assess risk across 25+ attack vectors

Improve posture continuously

End-to-End Factory Security

Integrated with HPE AI Factory stack

Secure from silicon to application

Unified observability and response

Lifecycle security and partner ecosystem

Trusted Through Our Partnership With HPE

Daxa is a security partner in the HPE AI Factory ecosystem, enabling secure data pipelines, protected retrieval, and governed agentic workflows on HPE’s enterprise infrastructure.

This joint approach ensures AI factories are secure from the hardware layer to the model layer.

View announcement

"HPE’s collaboration with Daxa reinforces our commitment to helping enterprises adopt AI safely and at scale. By combining our AI infrastructure with Daxa’s AI governance layer, we are ensuring enterprises can innovate without compromising on security or compliance."

Vinod Bijlani

AI Practice Leader, HPE

Securing Your AI Factory. No Compromises.