Securing AI with Orca Security: A Must for Organizations Leveraging AWS
Author: Jason Patterson, Sr. WW Security PSA – AWS
Co-Author: Deborah Galea, Director of Product Marketing – Orca Security
Artificial intelligence (AI) is transforming industries around the globe, and research indicates that the AI software market is set to expand at a staggering rate of 19.1% annually, reaching an estimated $298 billion by 2027. Among the many cutting-edge services available, Amazon Bedrock and Amazon SageMaker stand out, leading a surge in AI adoption. However, with this rapid progress comes the critical need for robust AI security measures to protect against risks such as model poisoning and sensitive data breaches.
In this article, we will explore the potential risks associated with AI services, particularly on AWS, and share how Orca Security is poised to help organizations safeguard their AI environments.
Understanding AI Service Risks
AI services like Amazon Bedrock and Amazon SageMaker rely heavily on data for training models. If attackers gain access to this training data, they could tamper with it, potentially skewing outputs. Moreover, if that data contains sensitive information, malicious actors could exploit AI models to extract unauthorized data.
Orca Security employs its innovative SideScanning™ technology, scanning AWS environments without the need for agents. This allows for a seamless assessment of configurations, data storage, permissions, and security settings associated with these AI models.
Key Risks to AI Technologies
We draw attention to several high-risk scenarios impacting AI services:
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Prompt Injection: Cybercriminals may input deceptive prompts into large language models (LLMs), causing these systems to perform unintended actions, like disclosing sensitive information.
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Data Poisoning: An attacker can manipulate an AI training dataset to compromise the model’s accuracy and reliability.
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Model Poisoning: This refers to an attack that introduces security flaws or biases into an AI model.
- Model Inversion: In this scenario, an attacker reconstructs original training data from model outputs, potentially exposing confidential information.
Further insights into these risks can be found in the OWASP Machine Learning Security Top Ten list, alongside Orca’s recent “2024 State of AI Security Report,” which sheds light on the prevalence of these issues in cloud environments.
Top Security Challenges for AI
Organizations face several hurdles when securing their AI models:
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Rapid Innovation: The pace at which AI is developing often prioritizes usability over security.
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Shadow AI: Many AI models exist without the awareness of security teams, complicating identification and management.
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Diverse Datasets: Training datasets often come from multiple sources with varying security levels, making uniform security challenging.
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Emerging Technology: As AI security remains in its infancy, businesses frequently navigate this landscape without comprehensive resources or experienced professionals.
- Resource Misconfiguration: It’s easy to overlook secure configurations during the rollout of new AI services, inviting risks due to mismanaged permissions or settings.
Introducing AI Security Posture Management (AI-SPM)
AI Security Posture Management (AI-SPM) solutions are designed to help organizations safeguard their AI and machine learning infrastructures. They can detect standard risks found in AWS environments, like misconfigurations and over-permissioned accounts, while also addressing AI-specific vulnerabilities.
AI-SPM solutions not only aid in identifying security gaps but also ensure compliance with industry regulations through continuous monitoring and reporting.
How AI-SPM Works
The AI-SPM process begins by cataloging all AI projects and models employed within an enterprise’s AWS environment. Risk detection follows, identifying vulnerabilities based on their likelihood of exploitation and potential impact. Finally, the solutions generate remedial options to mitigate identified risks throughout the software development lifecycle, thereby addressing issues before they escalate.
Orca’s AI-SPM Capabilities on AWS
Orca Security employs its renowned agentless SideScanning™ technology to ensure that security measures cover more than just standard AWS resources. Our service extends deep, actionable insights specific to AI models used in Amazon Bedrock and SageMaker.
With capabilities addressing over 50 different AI models, Orca ensures that organizations can innovate confidently, all while maintaining a secure operational environment.
Comprehensive Insights with the Orca Platform
The Orca Platform allows users to gain a thorough AI inventory within their AWS framework, offering visibility into both managed and "shadow" AI deployments. Security measures include monitoring for sensitive data exposure within AI models and checking whether proper encryption protocols are in place.
In scenarios where training data includes sensitive information, such as Personally Identifiable Information (PII), Orca will alert teams to potential risks, enabling immediate corrective actions.
Furthermore, Orca detects unsafe exposures of keys and tokens across code repositories and initiates guided remediation through automated solutions, highlighting the ease with which vulnerabilities can be managed.
Conclusion
As organizations harness the immense potential of AI, the security challenges are becoming more nuanced and complex. By integrating Orca Security’s AI-SPM capabilities, particularly in environments utilizing Amazon Bedrock and SageMaker, companies can confidently explore AI possibilities without compromising security.
To learn more about how to protect your AI services, request a demo, or visit Orca Security on the AWS Marketplace.
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