The Top Generative A.I. Security Threats Facing the Restaurant Industry

How is A.I. being utilized currently in the restaurant industry?

From a front-of-house perspective, the restaurant industry is an early adopter of A.I. Many restaurants have announced initiatives to use A.I. for voice ordering applications in drive-thrus, kiosks, and other points of customer interaction. The goals of these use cases include efficiency in ordering processes, both for restaurants and customers, enhanced customer experiences, and an ability to enhance revenue by making customer- specific suggestions for new menu items and upsell options.

While front-of-house A.I. applications will be the primary areas where restaurant customers experience A.I., the use-case doesn’t stop there. There are significant advances in the use of A.I. in back-of-house restaurant operations as well. These applications are numerous, but generally fall into one of four categories: 

  1. Supply chain optimization
  2. Decision models
  3. Personnel-related applications (e.g., training, scheduling), and 
  4. Demand predictions
What are the new cybersecurity challenges that result from the adoption of A.I.?

Before we discuss the cybersecurity challenges, it’s important to note there will be other, non-security-related challenges with the rapid adoption of A.I. in the restaurant industry.  Anytime significant changes to customer interactions and business operations occur, outcomes may not always align with expectations. With generative A.I., for example, even well-trained models that produce expected results most of the time are known to produce “hallucinations” from time-to-time.  Unexpected outcomes will occur, and the enhanced customer interactions that are the goal of A.I. can become a negative interaction that sends a customer to a competitor. 

However, from a cybersecurity standpoint, we are currently seeing four areas of concern related to A.I.:

  1. Inadequate sandboxing and security of A.I. models and data environments
  2. Prompt hacking
  3. Security of third-party plugins
  4. Supply chain risk

When we consider sandboxing and the general security of A.I. models, it’s important to consider the immense value and amount of proprietary corporate and customer data that goes into developing useful models. These models often contain the most prized data one would never want to leave an organization.  Likewise, the purity and consistency of training data must be maintained for a model to work with expected consistency and accuracy.  Maintaining rigorous security operations, role-based and limited access to models, and a strict change process are all essential to preventing model and data theft, the introduction of poisoned data into models, and the unintended disclosure of confidential and Personally Identifiable Information (PII) data.

The second area, prompt hacking, is an exploit that utilizes carefully crafted prompts to mislead a Large Language Model (LLM) into performing unintended actions.  The most common form of prompt hacking is prompt injection where a user hijacks an LLM model’s output and can get the model to output an unintended response.  An example could be a response that is offensive to customers.  A second form of prompt hacking is prompt leaking where an attacker is able to cause a model to reveal previous prompts input by other users.  Again, this raises privacy concerns for models used broadly with many users.  Finally, jailbreaking is a form of prompt hacking where safety and moderation features set-up in models are bypassed, and a model will answer with content that is generally restricted – e.g., What is the best way to rob a bank?

The third area is the use of third-party A.I. programs and plugins, an item that is growing rapidly as restaurants rush to realize the benefits of A.I. The same risks that apply to in-house developed A.I. models and tools will occur with third-party plugins; however, these risks can be greater and unknown to companies since the plugins are managed by a third-party. Sensitive data exposure, security vulnerabilities, and susceptibility to items like prompt hacking are all potential issues with third-party tools.

And finally, as A.I. models and tools are used by supply chain partners, the security risks, and general business risks, will be amplified as critical supply chain partners will be susceptible to the same threats and issues as all organizations.  The care and effort a company puts into securing its models and third-party A.I. tools may be futile if the operations of a critical supply chain partner are impacted due to A.I. security issues.

How can restaurants prepare and secure their networks to address the security needs of A.I. models and data?

The same security hygiene required for restaurants’ corporate and franchisee operations is required for the protection of environments and networks housing A.I. data and models.  Restaurants should implement a multi-layered approach that focuses on technology, people, and security operations. 

As we witness each day in the news, comprehensive employee training is a crucial element. By helping associates recognize phishing attempts and social engineering tactics, restaurants can create a culture of cybersecurity awareness.

Another critical area specific to A.I. models is proper access controls. Rule-based access can ensure your LLM data is in the hands of the right people, making it less likely for errors to occur and creating fewer entryways for cybercriminals. Multi-factor authentication (MFA) across all systems and accounts adds another layer of security as well. Finally, regular updates and patch management are fundamental to ensuring vulnerabilities are addressed in a timely manner. 

Overall, restaurants need to ensure their LLMs are fully “sandboxed” from other internal company data sets. When restaurants and QSRs use proprietary or competitive data in their LLMs, it makes that data set a more valuable target for cybercriminals. Generative A.I. models require an equally extensive set of security tools and policies to ensure they are protected against the latest cyber threats. With generative A.I. becoming essential for businesses to stay competitive, full protection will be key to ensuring continuity and success. 

What tools can restaurants use to prevent cyberattacks?

Restaurants can deploy a range of tools to bolster their cybersecurity defenses. 

  • Firewalls and Intrusion Detection Systems (IDS) are essential tools for securing the network perimeter and detecting and blocking potential threats. 
  • Security Information and Event Management (SIEM) solutions provide real-time insights into potential security incidents by aggregating and analyzing security data from various sources. 
  • Endpoint Detection and Response (EDR) tools monitor endpoint devices for suspicious activity, allowing for quick detection and containment of potential threats. 
  • Penetration testing should also be employed to regularly test systems and applications for vulnerabilities, facilitating prompt patching and securing of any weaknesses. 
What will the future of A.I. look like in the restaurant industry’s cybersecurity practices?

The future of A.I. in the restaurant industry's cybersecurity landscape is promising. 

Advanced threat detection is anticipated where A.I. will proactively identify and respond to emerging threats, minimizing response time and damage.  Predictive analytics will play a significant role as well, which can analyze patterns, behaviors, and vulnerabilities to predict potential cyber threats and create proactive security measures.  Incident response will improve too. Automation will become more prevalent to enable real-time risk mitigation without requiring constant human intervention.  Lastly, A.I.-powered authentication methods, especially biometrics and behavioral analysis, will provide more secure and seamless access to systems and data.

Safeguarding the restaurant industry against cybersecurity threats demands a proactive stance, informed decision-making, and the strategic integration of A.I.-powered cybersecurity tools. Restaurants should stay updated on the evolving threat landscape and be prepared to adapt their cybersecurity practices to effectively mitigate risks. The excitement of generative A.I. in the industry is real – but only if done thoughtfully.