The pharmaceutical industry is undergoing a digital transformation like never before. Emerging technologies such as artificial intelligence, the internet of things, and real-time analytics have revolutionized manufacturing. Predictive maintenance, intelligent batch scheduling, and machine learning enhanced inspections have created a modern production floor that is smarter, faster, and more connected.
This transformation, however, comes with an underestimated cost: cybersecurity risk. Manufacturing is no longer confined within physical walls or protected by air-gapped networks. Instead, operations are deeply interconnected with enterprise resource planning systems, clinical data lakes, cloud-native platforms, and vendor-managed systems. In this environment, a cyberattack is not simply a technical event it can disrupt supply chains, delay batch releases, compromise drug quality, and directly threaten patient safety.
The Rise of AI, and the Broadening Attack Surface
The integration of artificial intelligence and connected devices has fundamentally expanded the attack surface. Vision systems powered by machine learning improve defect detection and accelerate batch release, but they also raise new risks: what if a threat actor manipulates the model, falsifies upstream sensor data, or compromises camera firmware?
With connected sensors regulating critical thresholds, robotic arms executing real-time adjustments, and algorithms making micro-decisions, even minor breaches can escalate into regulatory violations, product recalls, or compromised drug integrity. The challenge has evolved beyond protecting networks it is now about securing data pipelines, machine learning models, operational technology, and the entire digital thread of pharmaceutical production.
Real-World Threats with Real Consequences
Pharmaceutical manufacturing operates under higher stakes than most sectors. Life-saving products, stringent regulatory frameworks, and globally distributed supply chains mean that cyber incidents have both financial and public health consequences.
Documented incidents include ransomware attacks that shut down production and delayed the release of temperature-sensitive biologics, as well as breaches where proprietary drug formulations were exfiltrated through compromised contractor accounts. Supply chain intrusions have inserted malicious code into vendor software, while adversarial data poisoning has degraded the performance of quality-control models. These are not theoretical concerns they are real events with direct impact on patient safety and global access to medicines.
Embedding Security into AI-Driven Manufacturing Platforms
Cybersecurity must be built into pharmaceutical manufacturing from design to deployment. The key five best practices are emerging as essential, reinforced by real-world examples from the sector.
- Adoption of zero trust architecture – Every identity, whether internal or external, should be verified continuously and granted only the least privilege required. Multi-factor authentication, just-in-time access, and privileged session monitoring are critical to preventing misuse. A global manufacturer facing unauthorized access through a contractor’s account mitigated such risks by implementing zero trust and role-based access using platforms such as Microsoft Entra ID and Unity Catalog.
- Securing artificial intelligence pipelines with transparency – Metadata-driven orchestration through platforms like Azure Data Factory and Databricks allows every data transformation and model output to be logged, versioned, and linked to an auditable trail. This approach ensures traceability under regulations such as Title 21 of the Code of Federal Regulations Part 11 (21 CFR Part 11) and provides evidence when a model-driven decision impacts batch release.
- Proactive anomaly monitoring – Artificial intelligence-based anomaly detection can scan user behavior, network activity, and application logs for abnormal patterns, such as after-hours data extraction or mismatched geolocation access. In one incident, abnormal traffic volumes flagged by anomaly detection tools allowed rapid containment of a ransomware attempt before production was affected.
- Securing the supply chain. As reliance on external vendors and software-as-a-service platforms grows, risk extends beyond the factory. Organizations are now requiring formal security attestations from vendors, conducting continuous posture assessments, and piloting blockchain-based audit trails to verify authenticity of critical components. This provides assurance against tampering and strengthens resilience in distribution chains.
- Cultivating workforce awareness. Manufacturing engineers, data scientists, and quality professionals are increasingly on the front line of digital operations, yet many lack cybersecurity training. Regular awareness sessions and simulated scenarios have shown measurable reductions in phishing susceptibility and improved response to cyber events. Embedding this culture of vigilance ensures that cybersecurity is not seen as the responsibility of information technology teams alone, but as an organization-wide priority.
Why Compliance Alone Isn’t Enough
Regulatory frameworks such as the Health Insurance Portability and Accountability Act (HIPAA), Good Automated Manufacturing Practice (GxP), International Organization for Standardization – ISO 27001, and Title 21 of the Code of Federal Regulations Part 11 (21 CFR 11) provide strong foundations. Yet compliance with these frameworks does not equate to security. Systems may meet documentation requirements while leaving unpatched vulnerabilities or insider threats unchecked. Compliance is static, while adversaries evolve rapidly with new forms of ransomware, adversarial artificial intelligence, and supply chain infiltration.
The leading organizations operationalize compliance by integrating it into daily development and deployment. This includes embedding automated control validation in continuous integration and deployment pipelines, conducting continuous penetration testing, and tailoring red teaming exercises to the realities of operational technology and artificial intelligence systems. In this way, compliance becomes the baseline rather than the ceiling of security maturity.
Looking Ahead: A Resilient Path Forward
Pharmaceutical manufacturing is advancing toward digital twins, edge artificial intelligence, and predictive analytics. These innovations will enable greater efficiency and agility but will also expand the attack surface. Preparing for this future means embracing resilience as a principle. Artificial intelligence will be needed to defend artificial intelligence by detecting anomalies within models and data pipelines. Data-centric security will focus on protecting information in every state at rest, in motion, and in use through methods such as homomorphic encryption and confidential computing. Cryptographic approaches will need to evolve to withstand quantum-enabled threats. Governance will increasingly require cross-domain collaboration, where security, operations, compliance, and data teams co-design secure systems rather than layering controls afterward. Continuous threat simulation will become standard, replacing periodic audits with ongoing resilience testing.
Final Thought
Artificial intelligence is transforming pharmaceutical manufacturing into a smarter and more interconnected ecosystem. This transformation, however, comes with an expanded surface for cyber risk. Cybersecurity must be treated as a design principle, not a regulatory checkbox. By embedding zero trust access controls, transparent and auditable pipelines, advanced anomaly detection, resilient supply chains, and a culture of awareness, organizations can strengthen their defenses. The future of manufacturing depends on building secure-by-default systems that protect intellectual property, regulatory trust, and most importantly, the safety of patients worldwide.
Let’s not wait for the next breach to act. Let’s lead with resilience.
About Rama Devi Drakshpalli
Rama Devi Drakshpalli is a Data & Analytics Solution Architect at Tech Mahindra with nearly two decades of experience in cloud-native data platforms, pharmaceutical analytics, and AI-driven healthcare security. She specializes in Azure, Databricks, and governance frameworks that enable compliance-driven modernization and digital transformation. Beyond her industry leadership, she contributes as an author, researcher, and reviewer in the fields of AI, cybersecurity, and data science in Healthcare analytics.
