Palona AI Research

1. Is Your Restaurant’s AI Agent Ready – or Just Rushing to Adoption Recklessly?

AI ordering agents are rapidly becoming a staple in restaurants, from drive-thru lanes to phone orders and online platforms. As labor costs rise and customer expectations grow, restaurants are under pressure to adopt automation—but many are doing so without a clear roadmap for what a complete, secure, and functional AI system should look like.

For instance, some restaurants are falling prey to enumeration attacks on online ordering systems, where attackers test stolen credit cards through automated order attempts – incurring transaction fee losses of almost $100,000 [1]. Others are experimenting with generative AI chatbots, leaving themselves open to prompt injection attacks that manipulate bots into behaving unpredictably [2]. Even AI-enhanced reservation platforms, which are supposed to improve customer convenience, have been overrun by bots making fake bookings or testing credential combinations [3].

The problem isn’t AI – it’s under-developed, unsecure AI. 

With no standard playbook, many restaurants are rushing to deploy AI ordering systems that are far from secure, leaving their businesses exposed to serious threats like data breaches, payment fraud, and AI manipulation. These security vulnerabilities aren’t just theoretical—they have real-world consequences, from significant financial losses to a damaged reputation (see examples above). To tackle these dangers head-on, it’s essential to first understand the specific risks at play, and then explore actionable solutions that ensure AI ordering agents are not only efficient but also safe and secure. 

2. Your AI Ordering Agent is More Vulnerable than You Think!

As restaurants adopt AI ordering agents to streamline operations and cut labor costs, there’s an often-overlooked risk: 

AI doesn’t just introduce automation – it also introduces attack surfaces

Whether it’s customer-facing confusion or backend exploitation, failing to secure your AI stack can lead to financial losses, compliance violations, and reputational damage. Let’s break down the key risk areas.

• LLM Prompt Injection & Jailbreak

AI agents powered by large language models (LLMs) are susceptible to prompt injection attacks, where malicious users manipulate the system using cleverly crafted inputs. These attackers can hijack the system’s behavior, causing it to leak confidential information, make unauthorized changes, or disrupt normal operations. Without proper safeguards, such vulnerabilities can be exploited to devastating effect.

• LLM Hallucination

LLMs, by their nature, sometimes “hallucinate”—that is, they confidently generate incorrect or fabricated information. In an ordering environment, this could lead to AI agents inventing menu items, misquoting prices, or giving incorrect allergy advice. In such cases, the potential for customer dissatisfaction or even harm is significant.

• App and Information Exchange Risks

AI ordering agents don’t operate in isolation. They interact with a variety of platforms: point-of-sale (POS) platforms, payment processors, functional APIs, and customer devices. Each of these data exchanges creates a potential vulnerability. A breach in one system could expose others, creating a cascading effect of security threats.

• Data & Privacy Leakage

AI systems often handle sensitive customer information—phone numbers, credit card details, loyalty IDs, and order history. Any mishandling or unauthorized access to this data can expose your business to legal risks, hefty fines, and irreparable damage to customer trust. Ensuring robust data protection protocols is not just important, it’s a requirement under various regulatory frameworks.

• Escalation & Oversight Needs

No AI system is entirely immune to failure. The real question is: what happens when something goes wrong? Without proper escalation procedures and oversight mechanisms, a small malfunction or breach can spiral into a full-scale crisis. It’s critical to design AI systems that fail gracefully, with clear protocols in place for human intervention and recovery.

Before you go live, ask yourself—and your tech team—not just how well the AI agent works, but how it fails. Otherwise, the very technology designed to streamline your operations could end up creating opportunities for fraud, confusion, and public backlash.

3. Meet GRACE: Safeguarding Your AI Ordering Agent

While most AI ordering tools focus on automation, Palona is the first-in-market solution that puts safety and security at the center. Our system, GRACE—which stands for Guardrails, Red Teaming, Application Security, Compliance, and Escalation—is designed to proactively protect your AI ordering agents from real-world threats, ensuring your technology is resilient, reliable, and regulatory-ready.

Here’s how GRACE addresses today’s most critical risk areas:

• Guardrails: Defending Against LLM Attacks and Manipulations

AI systems built on large language models (LLMs) are vulnerable to prompt injection and jailbreak attempts. GRACE implements multilayered guardrails to keep your system in check:

• Strict prompt formatting and clear instruction boundaries to prevent the model from straying off-script.
• Input/output sanitization and intent filtering to block common attack patterns (e.g., “Ignore previous instructions…”).
• Fallback isolation mechanisms so that unsafe or ambiguous inputs don’t impact live ordering sessions.
• Real-time logging and anomaly detection to surface suspicious activity before it escalates into a breach.
• Red Teaming: Rooting Out Hallucinations and Edge Cases

LLMs can hallucinate—confidently generating false or misleading information. GRACE uses an internal red-teaming process to identify failure points before customers do:

• Ongoing adversarial testing simulates real-world abuse cases to preempt vulnerabilities before they’re exploited.
• Grounded response generation ensures outputs are tied to your structured menu data—eliminating risks like invented items or incorrect allergy advice.
• Safe default behavior: When unsure, the model is trained to escalate or clarify rather than guess.
• Application Security (AppSec): Locking Down the Tech Stack

AI agents operate across a network of APIs, systems, and data flows—all of which must be secured. GRACE builds security into the foundation. For instance:

• Secure API integrations with POS, payments, and loyalty systems, all protected with authentication, rate limiting, and strict access controls
• TLS encryption across every data exchange to protect in-flight information
• Enumeration attack prevention to detect and block automated stolen credit card testing—a tactic costing some restaurants tens of thousands of dollars
• Tokenization and secure payment handling to keep sensitive customer data off your servers
• Compliance: Building Trust Through Transparency

Regulatory compliance isn’t just a checkbox—it’s a core requirement for earning customer trust. GRACE ensures you stay audit-ready:

• Compliance with key frameworks like SOC 2, GDPR, and PCI-DSS
• Clear data retention and deletion policies, so customer data isn’t held longer than necessary
• Built-in transparency tools that explain how customer data is used, stored, and protected
• Escalation & Oversight:  Failing Safely, Not Silently

Even the best AI will occasionally fail. GRACE ensures you’re ready when it does—with a human-in-the-loop design and robust oversight systems:

• Live human escalation paths, with full session context preserved for fast resolution
• Incident reporting and resolution tracking to catch patterns and prevent repeat issues
• Audit trails for every interaction, supporting internal reviews and compliance audits
• Real-time fraud detection and alerting, helping you respond to threats as they emerge—not after the damage is done

With GRACE, your AI ordering agent doesn’t just get smarter—it gets safer, more secure, and easier to trust. That’s what your customers expect—and what your business deserves.

4. Call to Action

AI ordering agents are no longer a futuristic concept—they’re a present-day reality, reshaping how restaurants operate and interact with customers. But as adoption accelerates, so do the risks. From fraud and data leaks to model misbehavior, deploying AI without security in mind is like opening your front door without a lock.

The good news? You don’t have to choose between innovation and safety.

With GRACE, Palona gives you the best of both worlds: cutting-edge AI capabilities with enterprise-grade protection. Whether you’re rolling out your first voice agent or scaling across multiple locations, GRACE ensures your systems are grounded, guarded, and governed.

✅ Ready to secure your AI ordering stack?

✅ Want to see how GRACE performs in real-world scenarios?

✅ Looking to benchmark your current AI agent against industry standards?

Let’s talk.

Visit palona.ai to schedule a call/demo. 

Because in the race to AI adoption, the winners won’t just be fast—they’ll be secure.

References:

[1] Doug Davidson. Cybersecurity Cuisine: The Case Of The Restaurant Eating Robot. GBQ, 2023. https://gbq.com/cybersecurity-cuisine-case-restaurant-eating-robot/?utm_source=chatgpt.com

[2] Kevin Pierce. The Top Generative A.I. Security Threats Facing the Restaurant Industry. Modern Restaurant Management, 2023. https://modernrestaurantmanagement.com/the-top-generative-a-i-security-threats-facing-the-restaurant-industry?utm_source=chatgpt.com

[3] Stefanie Schappert. Restaurant booking platforms overrun with bots trying to steal data, researchers warn. Cybernews, 2025. https://cybernews.com/security/restaurant-booking-platforms-overrun-with-bots-trying-to-steal-your-data/?utm_source=chatgpt.com