AI Security
Protect AI at the pace of business. Empower your teams to innovate safely. Eliminate blind spots and govern your cloud AI services with a platform built to see the risks traditional tools miss.
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The Challenge
The AI Attack Surface is Sprawling at an Exponential Pace
Every AI model, coding copilot, MCP server, and cloud AI service your organization adopts expands the attack surface faster than security teams can track. Most weren’t built with security in mind, and traditional tools weren’t built to see them.
Security teams can’t see where shadow AI services and agents get deployed.
Security risks for AI go beyond the prompt level and impact every part of the application lifecycle.
The ownership gap for AI risk is unresolved, and attackers don’t wait for org charts.
Our Approach
End-to-End Visibility and Protection: Code, Posture, and Runtime
One platform for cloud and AI security. Orca expands core AppSec, SideScanning™, and Sensor capabilities to deliver the same visibility, risk insight, and deep data for AI that it does for other cloud resources.
AI-SPM
Inventory every model, pipeline, training dataset, and AI package in your environment. Agentless coverage means no blind spots, even as your AI infrastructure scales faster than your team can instrument it.
MCP server activity
Prompts are only inputs to an unpredictable system. Orca shows you what the system actually did: where MCP servers are active, what prompts triggered them, and overall activity cadence to understand the bigger context.
Prompt-level risk analysis
Prompts are analyzed as they happen for secrets leakage, PII exfiltration, prompt injection, and other suspicious patterns. Fine-tune governance policies to your organization’s actual risk profile, not generic guardrails.
Real-time AI activity detection
Orca Sensor captures all LLM requests and MCP activity, maps it to originating workloads and identities, and surfaces risk in real time. This data is enriched with cloud context your SOC already understands.
Report
2025 State of Cloud Security
See what AI is running in production
Get a comprehensive view into all AI running in your cloud-native applications (including runtime activity) and the risk they introduce into your environment, whether they are cloud-managed AI services, self-hosted AI software, MCP servers, or specific AI models.
Drill into what AI is doing to understand the business use case for AI
Point solutions for AI security either give a broad overview or narrowly scoped telemetry about how AI is used. The Orca Platform does both. In addition to AI-SPM dashboards, Orca Sensor delivers the stream of AI activity on workloads, providing a granular look at how AI is being used.
Manage risk introduced by AI-generated code
Understand how your developers are using AI to generate code and what risk these decisions introduced. Analyze the performance of human vs AI-generated code.
Prioritize AI Risk with Context Baked-In
Connect the dots across exposure details, asset context, IAM, and data sensitivity to prioritize the risk to workloads running AI. Get the same insights from our Unified Data Model as we evolve for the AI era.
Related Resources
AI Security Solution Brief
As organizations rapidly adopt AI, security teams struggle to keep pace with an expanding attack surface and "shadow AI" services…
SACR Report: The Convergence of AI and Data Security
As AI evolves into an autonomous execution layer, traditional security models are becoming obsolete. This SACR report explores Orca Security’s…
2026 TAG Enterprise AI Security Handbook
Move from AI experimentation to real-world AI security and optimization at scale. AI security is rapidly evolving, and most teams…
Frequently Asked Questions
AI Security Posture Management is an emerging field of cloud security that involves addressing security risks and compliance issues associated with using AI models. It involves a collection of strategies, solutions, and practices designed to enable organizations to securely leverage AI models and LLMs in their business.
Like other cloud assets, AI models and LLMs present inherent security risks, including limited visibility, accidental public access, shadow data, unencrypted data, unsecured keys, and more.
In recent years, AI usage has increased dramatically. More than half of all organizations already use it in the course of their business. AI’s widespread adoption, coupled with the security risks of using AI services and packages, puts organizations at heightened risk of security incidents. The below figures help illustrate this risk:
- 94% of organizations using OpenAI have at least one account that is publicly accessible without restrictions.
- 97% of organizations using Amazon Sagemaker notebooks have at least one with direct internet access.
Despite its advantages, AI poses significant security risks that organizations must consider and address, including:
- Lack of visibility: Security teams don’t always know which AI models are currently in use and aren’t able to discover shadow AI.
- Data exposure: Misconfigured public access settings, exposed keys, and unencrypted sensitive data can cause data leakage.
- Data poisoning: Bad actors can potentially tamper with data and insert malicious content.
- Key exposure in repositories: These keys allow bad actors to make API requests, tamper with the AI model, and exfiltrate data.
AI Security Posture Management involves several important activities to cover end-to-end AI risks, including:
- Cloud scanning and inventorying: The entire cloud estate is scanned to generate a full inventory of all AI models deployed within the cloud environment(s).
- Security posture management: Secure configuration of AI models is ensured, including network security, data protection, access controls, and IAM.
- Sensitive data detection: Sensitive information in AI models or training data is identified and alerts are generated so appropriate action can be taken.
- Third-party access detection: Teams are alerted when sensitive keys and tokens to AI services are exposed in code repositories.
The Orca Cloud Security Platform secures your AI models from end-to-end—from training and fine-tuning, to production deployment and inference.
- AI and ML Inventory and BOM: Get a complete view of all AI models that are deployed in your environment – both managed and unmanaged.
- Security posture management: Ensure that AI models are configured securely, including network security, data protection, access controls, and IAM.
- Sensitive data detection: Be alerted if any AI models or training data contain sensitive information so you can take appropriate action.
- Third-party access detection: Detect when keys and tokens to AI services— such as OpenAI, Hugging Face—are unsafely exposed in code repositories.
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