Securing your AI via AI Gateways

Hadar Yakir

and

April 28, 2025

min read

As organizations increasingly integrate large language models (LLMs) into their workflows, the operational demands on AI infrastructure continue to grow. AI gateways have emerged as a powerful tool to manage this complexity.

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What is an AI gateway?

Traditional AI integrations often struggle with scaling efficiently. Direct API calls to LLMs can lead to bottlenecks, high costs, and a lack of control over usage. Managing multiple AI models, especially at scale, becomes cumbersome without a centralized layer to streamline requests.

Moreover, issues like compliance and observability become more complicated as AI systems grow. Organizations often find themselves facing unpredictable costs, difficulty tracking performance, and limited insights into the AI model’s behavior in production.

AI gateways tackle these pain points by offering centralized management, better resource allocation, and enhanced monitoring, making AI integrations much more efficient and manageable.

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Why AI Gateways Need Dedicated Security Layers

AI gateways excel at managing API requests, optimizing resource usage, and providing visibility into model performance. However, they are not built to identify or stop security threats native to AI workflows.

Without a purpose-built AI security layer, organizations face growing exposure to risks such as:

Prompt-based attacks: Gateways often log or forward prompts but lack the ability to detect or block harmful inputs. A seemingly safe prompt can be manipulated by users into leaking sensitive data or bypassing model safeguards.
Data exposure: LLMs frequently interact with personally identifiable information (PII), proprietary business data, or sensitive internal logic. Standard gateways don’t inspect this content for privacy violations, creating compliance blind spots.
Adversarial manipulation: From prompt injections to output tampering and meta-prompt exploits, attackers are developing increasingly complex techniques to manipulate AI behavior.
Compliance and audit gaps: Industry stantards like ISO, HIPAA, or internal governance policies need clear evidence of data protection and risk management—requirements that gateways alone can’t meet.

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Building a Secure AI Infrastructure: Pillar + Portkey

To fill these critical security gaps, organizations must pair their AI orchestration tools with systems built specifically to protect AI systems. That’s where Pillar comes in.

Pillar integrates seamlessly with platforms like Portkey to deliver end-to-end security for AI applications, from input to output.

Together, Portkey and Pillar offer:

Real-Time Threat Detection and Prevention: Continuously monitors and blocks prompt-based attacks, unsafe queries, and other malicious inputs—stopping adversarial actions before they reach your models or end users.
Comprehensive Data Protection: Automatically detects and protects sensitive data, including PII and proprietary content, across both model inputs and outputs to mitigate exposure and reduce risk.
Holistic Security Monitoring: Extends visibility across your entire AI workflow: prompts, tools, models, meta-prompts, outputs, and system-level events. Enables real-time risk insights and actionable alerts.
Alignment with Industry Standards: Implements best practices from frameworks like the OWASP Top 10 for LLMs and MITRE ATLAS, helping organizations bake security into every layer of their AI stack.

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Enhanced security through customized controls and automation

By embedding security at the gateway level, Portkey and Pillar empower organizations with advanced control and automation capabilities, including:

Proactive Risk Management: Automatically blocking or flagging high-risk requests before they impact models.
Detailed Audit Logging: Comprehensive security logs that facilitate regulatory compliance, audits, and investigations.
Automated Security Insights: Collection and analysis of security events, enabling continuous improvement of security strategies.
Intelligent Fallback Mechanisms: Automatic switching to alternative AI models when security risks are detected, ensuring service continuity.
Secure Request Retries: Built-in retry mechanisms employ enhanced security parameters, minimize disruption, and maintain workflow integrity.

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Security as a core infrastructure component

Operational efficiency and robust security can coexist seamlessly within AI workflows. By treating security as a core infrastructure component—not an afterthought—teams can build AI systems that are scalable, compliant, and resilient by design.

With Portkey providing universal access to over 250 LLMs through a unified API, processing billions of tokens daily, and Pillar ensuring ongoing threat monitoring and protection, organizations can confidently scale their AI deployments without compromising security.

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Ready to protect your AI workflows?

Learn more on how Portkey and Pillar can help you build a secure, intelligent, and compliant AI infrastructure: https://portkey.ai/docs/product/guardrails/pillar

3 steps to integrate:

Add Pillar’s API key to Portkey
Create Guardrail Checks by selecting the Pillar evaluators you want
Set up actions on the Guardrails and then add the Guardrail to a request Config.

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FAQs

Why can't AI gateways alone protect against prompt injection and adversarial attacks?

AI gateways are built to manage API requests, optimize resources, and monitor model performance — not to detect or block security threats. They typically log or forward prompts without inspecting them for malicious intent. Stopping prompt injections, output tampering, and meta-prompt exploits requires a purpose-built AI security layer operating alongside the gateway.

What compliance and audit gaps do AI gateways create for regulated industries?

Standard AI gateways do not inspect model inputs and outputs for PII, proprietary data, or privacy violations, creating compliance blind spots. Frameworks like HIPAA and ISO require clear evidence of data protection and risk management that gateways alone cannot provide. Without a dedicated security layer, organizations lack the audit logs needed to meet these regulatory requirements.

How does Pillar Security integrate with Portkey to secure AI workflows?

Pillar integrates with Portkey to deliver end-to-end security across AI inputs and outputs. The integration enables real-time threat detection, automatic PII and proprietary data protection, and security monitoring across prompts, tools, models, meta-prompts, and system events. Setup requires three steps: adding Pillar's API key to Portkey, creating Guardrail Checks with Pillar evaluators, and attaching the Guardrail to a request configuration.

What security frameworks does the Pillar and Portkey integration align with?

The Pillar and Portkey integration implements best practices from the OWASP Top 10 for LLMs and MITRE ATLAS. These frameworks guide how security is embedded across every layer of the AI stack, helping organizations align their AI deployments with recognized industry standards for threat modeling, risk management, and adversarial attack mitigation.

How does intelligent fallback and secure retry functionality work in a Pillar-secured AI gateway setup?

When Pillar detects a security risk during an AI workflow, an intelligent fallback mechanism automatically switches traffic to an alternative LLM, preserving service continuity without exposing the system to the threat. Secure request retries are also built in, applying enhanced security parameters on each retry to minimize disruption and maintain the integrity of the overall workflow.