CYBERSECURITY AND FOOD DEFENSE

By Robert A. Norton, Ph.D., Professor of Veterinary Infectious Diseases and Coordinator, National Security and Defense Projects, Office of the Senior Vice President of Research and Economic Development, Auburn University; and Marcus H. Sachs, P.E., Senior Vice President and Chief Engineer, Center for Internet Security

Malevolent, Misaligned, and Misused: How Emerging AI Threats Endanger the Biological and Business Foundations of Food Safety

AI is no longer just a tool to be adopted; it is a national and business security domain that must be secured if food corporations intend to remain in business

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Artificial intelligence (AI) has rapidly moved from a promising add‑on in food safety programs to a foundational capability that shapes how hazards are detected, controlled, and communicated. AI‑enhanced biosurveillance, integrated sensor networks, and intelligent analytics have been framed as critical enablers of safer, more efficient food systems. At the same time, the field has begun to acknowledge a darker reality: the same tools that drive efficiency and predictive power can also be turned against the food system itself.¹

Earlier articles in this food defense series highlighted how AI can support the development of robust, validated food safety programs and laid out the vision for an AI‑enhanced biosurveillance system across the farm‑to‑fork continuum. Building on that foundation, this article examines the next logical step by contextualizing the rapidly evolving, almost daily to weekly, AI‑related threats that could undermine both the biological underpinnings of food safety. These foundational elements include the detection of pathogens, contamination dynamics, and control measures, as well as the business structures that support these functions including corporate liability and branding, supply chains, regulatory relationships, and public trust. The central argument is straightforward: AI is no longer just a tool to be adopted; it is a national and business security domain that must be secured if food corporations intend to remain in business.

The New AI Landscape in Food Safety

The use of large language models, generative models for images and code, agentic AI, and specialized AI-powered tools for molecular design have lowered the barrier for complex analysis and experimentation within food safety systems. Techniques that once required teams of experts and months of modeling—such as scenario analysis for pathogen survival under different conditions—can now be approximated in hours by practitioners with modest technical skills.

On the opposite side, malign actors are increasingly using AI tools oriented toward counter-social actions (e.g., anti-animal protein consumption or anti-corporate misinformation campaigns), while criminals and nation-state actors seek to undermine the safety and availability of the food supply.

AI has become deeply embedded in food sector operations, creating a new "attack surface" and new targets for malicious activity. Across the supply chain, AI tools support predictive maintenance, quality control, and anomaly detection in processing lines; they inform inspection prioritization, optimize cold chain logistics, and drive decision‑making in traceability and recall programs. These implementations have improved efficiency and sensitivity, but they have also created a "target-rich environment" for the malign actor. Every AI‑enabled device, sensor, and decision pipeline becomes a potential target for malicious manipulation, misconfiguration, or adversarial interference. As more safety‑critical decisions are delegated to AI, the potential impact of failure—whether accidental or deliberate—is growing.

Biological Threats: AI‑Enabled Manipulation

AI‑Enabled Pathogen Optimization and Misuse
Many of the models now being developed to support microbial risk assessment and process optimization are explicitly designed to predict how pathogens behave under varying environmental and process conditions. These tools can estimate the survival or growth of organisms like Salmonella, Listeria, or pathogenic E. coli across combinations of temperature, time, humidity, pH, and packaging variables. Used properly, such models help validate control measures and strengthen Hazard Analysis and Critical Control Points (HACCP) plans.

The dual‑use risk is that similar modeling approaches could be turned toward optimizing harm. An adversary with access to AI‑enabled predictive models could search for conditions that maximize pathogen persistence while minimizing detectability, tailored to specific products and processing lines. Instead of exploring how to guarantee a log‑reduction, for example, an attacker could explore how to avoid it. Generative models increasingly assist with literature review, protocol design, and "what‑if" exploration; in the wrong hands, they could streamline the design of contamination campaigns that target high‑value products, vulnerable consumer populations, and/or vulnerable process points or critical distribution hubs.

Using AI to Evade Biosurveillance and Sensor Networks
In earlier work,¹ dense and layered sensor networks, from pre‑harvest environmental monitors to in‑line process sensors and retail‑level systems, were presented as a plausible future for AI‑enhanced biosurveillance. When combined with machine learning analytics, these networks promise earlier detection of contamination and faster response to emerging risks.

However, even such AI-driven biosurveillance systems depend on predictable relationships between signals and alerts, and adversaries can exploit that predictability. By modeling the detection thresholds, false‑positive tolerances, and filtering logic of biosurveillance systems, attackers could design contamination patterns that remain below automated alert thresholds long enough to allow wide distribution. They might also use adversarial machine learning techniques to carefully craft inputs that cause models to misclassify signals in order to hide genuine anomalies behind patterns the AI system has been trained to ignore. Spoofed sensor data, injected into networks or supervisory systems, could mask real events or create "noise" that desensitizes operators to genuine warnings.

Dual use is again at the core. The same optimization routines used to tune sensors for cost‑effective performance can also identify "quiet" windows in which attacks are most likely to go unnoticed. If biosurveillance models are not robustly designed and continually tested with adversarial behavior and capabilities in mind, then malign AI systems could potentially encode the playbook for evading detection. False positives can be managed in any biosurveillance system, while false negatives are never acceptable.

“Since many AI tools are designed to detect subtle patterns and correlations, they may be particularly vulnerable to carefully designed data poisoning.”

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Synthetic Outbreaks and Attribution Challenges
A more subtle, but equally concerning threat arises when AI is used not to manipulate biology directly, but to manipulate the data that describes it. Machine learning systems and statistical models play an increasingly important role in outbreak detection, source attribution, and regulatory decision‑making. These systems consume vast amounts of case and genomic data, lab results, traceability records, and environmental measurements.

If an attacker can inject falsified or manipulated data into these pipelines, then they may be able to shape the apparent trajectory of an outbreak. For example, synthetic outbreak curves could potentially alter genomic sequences, or edited lab results could misdirect investigations toward the wrong facility, region, or product category. Even modest perturbations of data could delay identification of the true source, prolonging exposure and expanding the scope of illness. Since many AI tools are designed to detect subtle patterns and correlations, they may be particularly vulnerable to carefully designed data poisoning. The biological and business consequences intertwine: misdirected investigations raise illness burden and operational costs, while also amplifying legal exposure and brand-related reputational damage for business organizations caught in the crossfire.

Business and Cyber‑Physical Threats: AI as an Attack Surface

Targeting AI‑Enabled Supply Chains, Traceability, and Logistics
Modern food businesses rely heavily on AI to manage the growing complexity involved in "just-in-time" distribution and logistics. Demand forecasting models influence production schedules; optimization algorithms determine routing, loading, and cold chain management; and AI‑driven traceability platforms support rapid recall decision‑making. These systems sit at the heart of both safety and profitability.

Data poisoning or algorithmic manipulation can rapidly cause cascading consequences. If training data for routing or forecasting algorithms is subtly corrupted, then the resulting models might routinely route temperature‑sensitive goods through longer, riskier logistical paths, eroding safety margins while appearing more efficient on paper. Manipulated traceability records aided by AI to ensure internal consistency could obscure the true origin or destination of contaminated lots, undermining recall effectiveness. In an extreme example, an attacker might seek to orchestrate simultaneous cold chain failures and traceability confusion, making it difficult to determine which products are unsafe and forcing overly broad, costly recalls. In such a case, the only option might be to dispose of the suspect food products, potentially negatively altering the bottom line if the attack were scaled.

Exploiting Automation and Robotics in Processing Environments
AI‑enabled automation and robotics in food processing plants promise greater consistency, significantly reduced labor costs, and improved safety by reducing human exposure to hazards. Machine vision systems inspect products; AI‑driven controllers adjust cooking, cooling, and cleaning parameters in real time; and autonomous equipment executes sanitation and handling tasks. These advantages, however, introduce new dependencies.

If an adversary gains access to control interfaces or underlying models, then they may not need to cause dramatic, easily detected failures. Subtle adjustments such as shortening a cook step slightly, intermittently skipping certain cleaning cycles, or misclassifying borderline defects as acceptable can slowly degrade safety performance. Since AI systems often present decisions as continuous, data‑driven optimizations rather than discrete on/off choices, operators may struggle to recognize malicious drift as it happens. Even without direct contamination, attackers could trigger plant shutdowns, regulatory interventions, or expensive audits by causing AI subsystems to behave erratically or unreliably.

AI‑Driven Disinformation, Brand Sabotage, and Regulatory Turbulence
Food safety is not only about biology and process control; it is also about corporate brand perception and trust. AI has sharply lowered the cost of generating high‑quality, synthetic text, images, and video. Deepfake "whistleblower" footage, fabricated consumer testimonials, or pseudo‑expert commentary can be created at scale, tailored to specific brands or product lines. As the sophistication of these malign outputs rises, detection becomes ever more difficult, but necessary to rapidly detect.

Coordinated disinformation campaigns could falsely claim contamination events, magnify and exploit legitimate consumer worries, and overwhelm corporate communication channels. Delay in response can be devastating to the brand. At the same time, AI‑based social listening and sentiment analysis tools used by companies and regulators become targets in themselves. Attackers could flood channels with synthetic content designed to trigger over‑reaction, force unnecessary recalls, or create the appearance of regulatory failure, which is particularly problematic in the current hyper-charged political environment. These tactics resonate with growing concerns about cognitive security such as the protection of decision‑making environments from manipulation. When public, corporate, and regulatory decisions are driven by polluted information ecosystems, even robust underlying safety programs may be undermined. The potential for blowback has increased exponentially from before AI's widespread availability.

Cross‑Cutting Governance Challenges: Data, Models, and Human Oversight

The threats described above are unfortunately not isolated technical issues; they reflect systemic governance challenges in how AI is adopted for food safety. First, data concentration is a double‑edged sword. Centralized platforms and cloud‑based AI services can improve consistency and enable powerful analytics across the supply chain, but they also create high‑value targets. A vulnerability in a widely used platform can propagate risk across many companies and regions at once. Under-protected businesses could see their business data evaporate, never to return.

Second, opacity in proprietary AI models complicates oversight at the corporate and government levels. Many safety‑relevant tools such as risk‑scoring engines, inspection prioritization algorithms, or supplier evaluation systems are developed by vendors that treat internal workings as confidential. While this is understandable from a commercial perspective, it limits the ability of regulators, auditors, and even internal safety leaders to evaluate how or if systems might fail or be misused when faced with sophisticated malign AI-driven attacks. When decisions about sampling plans, line release, or supplier acceptance are heavily influenced by algorithms that cannot be independently and thoroughly interrogated, organizations may be blindsided by failures they did not know to anticipate.

Third, there is currently a misalignment between high‑level AI ethics principles and the specific realities of food safety. Concepts like transparency and privacy take on particular meanings when they interact with traceability requirements, proprietary formulations, and complex, global supply chains. For example, increasing transparency around how models make decisions might reveal proprietary relationships; enhancing privacy can conflict with the need to share detailed data quickly during an outbreak or warn consumers. Navigating these trade‑offs requires deliberate governance, not just generic guidelines.

“Responding to AI‑related threats does not mean abandoning AI; rather, it means adopting a security‑by‑design mindset and building resilience into every layer of the AI ecosystem.”

Finally, human factors remain central to this issue. Over‑reliance on AI can lead to automation complacency, where operators are less likely or lack the skill sets to question and test outputs or even notice anomalies. Many organizations lack personnel with overlapping expertise in food microbiology, cybersecurity, and AI safety. This hybrid expertise is a critical national security and business need. University-based food safety education and training programs are not currently producing this type of cross-disciplinary expert. Closing this skills gap is essential if the industry is to manage AI as a complex, socio‑technical system rather than as a black box tool. The gap is so extensive that it has become a national security concern, given the critical need for an uninterrupted, readily available, economical, and safe food supply.

Building "Safe‑to‑Fail" AI Ecosystems for Food Safety

Responding to AI‑related threats does not mean abandoning AI; rather, it means adopting a security‑by‑design mindset and building resilience into every layer of the AI ecosystem. Three concrete priorities emerge.

Priority 1: Designing AI with Safety and Security by Default
Organizations should implement dual-validation pathways for all safety‑critical AI decisions. When an AI system recommends the release of a product batch, acceptance of a supplier, or initiation of a recall, a human‑in‑the‑loop checkpoint should be mandatory. Alternatively, independent models or rule‑based guardrails can be designed to serve as cross‑checks—if multiple systems agree, confidence rises; if they diverge, investigation is triggered. Even in these types of systems, a human should remain in the loop.

Adversarial testing and "red‑teaming" must become routine for any food safety-related AI system, whether it touches biosurveillance processes and systems, food process control, or traceability. Just as food companies test whether control measures can be defeated, they should also test whether their AI systems can be fooled, misled, or manipulated. This requires hiring or partnering with specialized AI defense security professionals and companies that can document an understanding of both machine learning and food safety and, equally importantly, have a proven track record in food systems. Over time, industry standards and regulatory frameworks should be collectively advanced by requiring documented evidence of high-level adversarial testing and documented resilience against known threat categories.

Priority 2: Strengthening Cognitive and Cyber Defenses
Food safety leaders must recognize that biosurveillance, outbreak investigation, and regulatory response are information‑intensive activities vulnerable to manipulation and disinformation. Integrated cognitive security monitoring that combines automated scanning of social media, aggregation of consumer complaints, analysis of lab data, and synthesis of sensor signals can help distinguish genuine signals from noise and deliberate manipulation.

Incident response playbooks should be developed that explicitly address malign scenarios in which AI‑generated content, spoofed data, or manipulated models complicate outbreak investigations. Key decision‑makers should be trained to ask: How do we validate that the data is authentic? What would convincing falsification look like? Which critical functions would we revert to manual control if our AI systems were compromised?

Organizations should also foster closer collaboration between food safety and food defense professionals, as well as cybersecurity experts, AI developers, and public health authorities at both the operational and strategic levels. A holistic approach to AI-driven food safety threats should become the corporate norm.

Priority 3: Scenario Planning and Exercises for AI‑Related Threats
The final element is preparedness. Too many organizations conduct tabletop exercises and drills based on familiar scenarios like contamination detection, supplier audits, or regulatory inspections. None of these exercises are realistic without incorporating AI‑centric challenges.

Consider a scenario in which a low‑dose contamination campaign is coordinated with an AI‑driven social media attack claiming broad contamination and manipulated traceability data pointing to the wrong supplier. Participants must navigate uncertainty about data authenticity, competing information, and pressure to act quickly. These exercises expose skills gaps, clarify decision authorities, and build confidence in response capabilities. They also help organizations understand the legal and reputational consequences of delayed or contested outbreak attribution when AI has muddied the evidentiary picture.

TABLE 1. Fake Internet Domains Registered as Part of Typosquatting Campaign

Navigating the Next Horizon of AI Risk in Food Safety

AI remains necessary to achieve more predictive, integrated, and cost‑effective food safety systems. The benefits are real: earlier and more sensitive detection of hazards, faster response to outbreaks, more efficient use of resources, and better‑informed decisions. However, ignoring the rapidly emerging threat space of biological, business, cognitive, and cyber‑physical domains risks eroding the very trust and resilience these systems are meant to strengthen.

The food industry and its regulators must treat AI not only as a tool but also as a new terrain for safety engineering and risk management. This requires building shared norms around responsible AI development and deployment, creating data‑sharing arrangements and joint exercises that keep pace with accelerating AI capabilities, and cultivating a workforce with overlapping expertise in food science, cybersecurity, and AI safety. The future of food safety depends not just on what AI can do for us, but on what we will do to secure it against misuse.

References

Norton, R.A., M. Sachs, and C.A. Young. "A Future View of AI-Enhanced Biosurveillance and Comprehensive Food Safety Programs." Food Safety Magazine December 2023/January 2024. https://digitaledition.food-safety.com/december-2023-january-2024/column-cyber/.

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