The transformation of industrial manufacturing into a digital-first, AI-driven ecosystem has introduced an era of unprecedented cyberrisk complexity. Organizations are shifting toward Industry 4.0 and Industry 5.0 models, integrating Industrial Internet of Things (IIoT), artificial intelligence (AI)-based automation, and cloud-driven manufacturing execution systems (MES) to streamline production, optimize efficiency, and minimize costs. However, the increasing interconnectivity between operational technology (OT) and IT infrastructures has exposed manufacturers to new cybervulnerabilities, particularly in areas such as ransomware threats, cyber–physical system (CPS) attacks, and supply chain infiltration.1
Cybersecurity in manufacturing requires a risk-based governance model that aligns with business continuity objectives, regulatory compliance mandates, and AI-driven security automation. Unlike traditional IT networks, manufacturing environments include complex dependencies on legacy industrial control systems (ICS), programmable logic controllers (PLCs), and real-time factory automation systems, making them uniquely vulnerable to cyberexploitation.2 A comprehensive cybersecurity strategy must address risk by implementing structured governance frameworks such as the US National Institute of Standards and Technology (NIST) Framework for Improving Critical Infrastructure Cybersecurity and International Society of Automation (ISA)/International Electrotechnical Commission (IEC) 62443.3 These models offer structured methodologies for risk management, security policy enforcement, and proactive cyberthreat mitigation.
As cyberadversaries develop AI-enhanced attack techniques and exploit supply chain vulnerabilities, manufacturers must prioritize the integration of self-learning defenses such as predictive AI analytics and Industrial Internet of Things (IIoT)-driven anomaly detection inside a zero trust security enforcement architecture. These security methods must be formally mapped to governance frameworks. Finally, the measures must be cryptographically anchored with blockchain-verified component provenance to protect networks and data effectively. There are strategic insights worth exploring that provide specific, field-tested steps manufacturers can take immediately to close common control gaps.
AI-Driven Predictive Threat Intelligence
AI-powered cybersecurity has emerged as a critical enabler of proactive threat intelligence, allowing manufacturers to detect, analyze, and neutralize cyberthreats before they escalate into full-scale breaches.4 Traditional cybersecurity methods rely on signature-based detection, reactive security policies, and manual incident response, which are insufficient for identifying AI-generated malware, zero-day exploits, and polymorphic cyberthreats. AI-driven threat intelligence leverages machine learning (ML) algorithms, behavioral anomaly detection, and real-time cyberrisk correlation models to provide predictive security automation.
Unlike traditional IT networks, manufacturing environments include complex dependencies on legacy industrial control systems (ICS), programmable logic controllers (PLCs), and real-time factory automation systems, making them uniquely vulnerable to cyberexploitation.A 2021 study on AI-enhanced cybersecurity resilience in industrial automation found that organizations implementing ML-based security information and event management (SIEM) platforms improved mean time to detect (MTTD) cyberthreats and accelerated security incident resolution.5 These AI-powered systems process millions of network events per second, identifying threat vectors through deep learning models that analyze network traffic, user behavior, and system anomalies.
AI-driven behavioral analytics significantly enhance insider threat detection, which remains one of the most overlooked areas of cyberrisk in manufacturing. AI-based cybersecurity platforms continuously monitor user behavior, detect unauthorized data access attempts, and trigger automated security responses when anomalies are identified. The effectiveness of AI-driven security automation can be further enhanced by zero trust security architectures, which ensure that every access request, data transaction, and system command is dynamically authenticated before being executed. AI-powered identity verification and continuous authentication provide real-time security oversight, preventing unauthorized system access and privilege escalation attacks.
Despite its advantages, AI-powered cybersecurity also presents unique risk. Adversarial AI attacks, data-poisoning techniques, and AI model evasion methods pose significant threats to ML-driven cybersecurity models.6 Organizations must implement robust AI model validation protocols, adversarial testing methodologies, and cybersecurity AI governance frameworks to mitigate risk. AI-driven detection models are now retrained on threat intelligence feeds and evaluated against evolving adversary tactics with test harnesses such as MITRE’s ATLAS, in line with the secure-update and provenance-verification practices prescribed by the NIST AI Risk Management Framework.7 Independent auditing and multilayered validation combine adversarial red-team testing and differential fuzzing. These and secondary rule-based guardrails that block policy-violating actions are codified in the European Union Agency for Cybersecurity’s guidance for critical-sector AI.8 Codes require operators to document every model change and enforce cross-model voting before any high-impact command is executed.
AI-driven cybersecurity must be continuously updated, audited, and reinforced with multilayered security validation models to ensure resilience against evolving cyberattack methodologies.
Regulatory Compliance and Cybersecurity Governance
Governance-driven cybersecurity models play a crucial role in ensuring that manufacturers adhere to regulatory compliance mandates, risk management best practices, and security auditing frameworks. As global regulatory bodies introduce stricter cybersecurity laws, manufacturers must align their cyberrisk governance strategies with compliance-driven security frameworks.
NIST, ISA/IEC 62443, and International Organization for Standardization (ISO)/IEC 27001:2022 provide structured methodologies for cybersecurity governance, continuous security auditing, and AI-driven regulatory compliance.9 Organizations that integrate compliance-based cybersecurity investment models into their security strategies significantly reduce financial penalties, prevent regulatory violations, and enhance overall cybersecurity maturity. Research into regulatory-driven cybersecurity investment strategies found that manufacturers prioritizing compliance-aligned security models reduced regulatory fines and improved security return on investment (ROI).10 Compliance-driven cybersecurity frameworks ensure that manufacturers implement proactive cyberrisk assessments, enforce security policy automation, and continuously audit cybersecurity resilience metrics.
The ISA/IEC 62443 industrial cybersecurity standard provides detailed guidelines for securing OT environments, industrial control networks, and interconnected smart factory infrastructures. Unlike IT-centric security models, ISA/IEC 62443 focuses on securing CPS, preventing cyberthreats that target industrial automation processes. ISA/IEC 62443 ranks among the top three regulatory drivers of OT-security investments.11 Manufacturers that reach ISA/IEC 62443-based cybersecurity control Level 1 report the lowest frequency of unplanned OT-system downtime after cyberintrusions.12 Zero trust security enforcement is an essential component of regulatory-driven cybersecurity governance, ensuring that all manufacturing assets, access points, and data transactions undergo continuous authentication and compliance validation. The integration of AI-powered regulatory compliance automation enables organizations to track compliance adherence in real time, identify security gaps, and automate regulatory reporting workflows.
As cyberthreats targeting industrial manufacturing continue to evolve, regulatory-driven security models will become a foundational aspect of global cybersecurity governance. Organizations must prioritize compliance-centric cybersecurity governance, AI-driven regulatory tracking systems, and zero trust compliance enforcement to maintain continuous cybersecurity resilience and regulatory alignment.
Blockchain-Enhanced Supply Chain Security
Manufacturing supply chains are highly interdependent, digitally interconnected, and vulnerable to infiltration. Attackers exploit weak vendor security postures, counterfeit component vulnerabilities, and software supply chain weaknesses to launch cyberattacks that impact enterprise-wide production ecosystems. The 2021 SolarWinds cyberattack, which compromised thousands of government entities and enterprises, serves as an example of the growing risk of supply chain cyberthreats.13
Blockchain authentication has emerged as a critical cybersecurity solution for securing industrial supply chains. A 2024 case study on blockchain-secured supplier authentication frameworks found that organizations integrating blockchain verification models reduced fraudulent component substitutions and improved supply chain transparency.14 By leveraging decentralized cryptographic validation, blockchain ensures that every vendor transaction, supply chain operation, and manufacturing component is cryptographically authenticated before being integrated into industrial workflows.
AI-powered supply chain security automation further enhances real-time vendor risk monitoring, predictive supply chain anomaly detection, and fraud prevention strategies. The combination of AI-driven supply chain risk scoring and blockchain-based authentication ensures that manufacturing supply chains remain resilient against cyberthreats.
Post-Quantum Cybersecurity Strategies
As quantum computing advances, traditional encryption mechanisms that currently protect manufacturing intellectual property, supply chain transactions, and ICS are at risk of becoming obsolete. Quantum computers can solve complex cryptographic problems several times faster than classical computers, making it possible to break encryption algorithms such as Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC), and even some blockchain-based security mechanisms. Manufacturers must prepare for post-quantum cybersecurity challenges by adopting quantum-resistant cryptographic frameworks, AI-driven quantum threat modeling, and zero-trust quantum authentication protocols.15 The US National Institute of Standards and Technology’s finalized FIPS 203 standard, ML-KEM, derived from the lattice-based CRYSTALS-Kyber algorithm, provides a production-ready quantum-resistant cryptographic framework, while AI-driven quantum threat modeling for IoT-SDN environments shows how quantum-enhanced ML can predict attack vectors and recommend mitigations.16 Cloudflare’s Zero Trust platform tunnels enterprise traffic through post-quantum cryptographic handshakes, offering a real-world example of a zero trust, quantum-safe authentication protocol that continuously verifies user identity and device context with quantum-resistant algorithms.17
A 2024 study on quantum-resistant cybersecurity in industrial automation found that manufacturers transitioning to post-quantum cryptographic models reduced encryption vulnerability risk.18 The adoption of lattice-based encryption, hash-based signatures, and quantum key distribution (QKD) technologies ensures that industrial security frameworks remain resistant to quantum decryption attacks. Unlike traditional cryptographic models, quantum-resistant algorithms are designed to withstand quantum computing-based brute-force decryption, ensuring long-term data confidentiality.
Manufacturers must integrate post-quantum cryptographic frameworks with AI-powered cybersecurity risk modeling to predict and mitigate emerging quantum decryption threats. AI-driven risk assessment models analyze advancements in quantum computing, potential attack vectors, and cryptographic vulnerabilities in industrial automation systems, allowing security teams to develop preemptive risk mitigation strategies. Zero trust quantum security models further enhance manufacturing cybersecurity resilience by enforcing continuous authentication, quantum-safe encryption validation, and AI-driven, real-time encryption key life cycle management. A hybrid post-quantum cryptographic framework, combining classical encryption models with quantum-resistant security algorithms, ensures that legacy industrial systems remain protected during the transition to full quantum-safe encryption models.
Manufacturers must also prepare for regulatory-driven quantum cybersecurity mandates as global cybersecurity agencies, such as NIST, the European Union Agency for Cybersecurity (ENISA), and ISO, develop post-quantum cryptographic standards for industrial applications. In January 2024, NIST released its first draft Federal Information Processing Standards (FIPS) for post-quantum cryptography, FIPS 203 (ML-KEM key-encapsulation), FIPS 204 (ML-DSA digital signatures), and FIPS 205 (SPHINCS+ hash-based signatures), providing manufacturers with algorithm-specific implementation requirements.19 While ISO/IEC JTC 1 has not yet issued a final international equivalent, ENISA has published a migration guideline that urges critical-infrastructure operators to adopt the NIST algorithms immediately and prepare governance documentation for the forthcoming ISO lattice-based specifications.20
Organizations that adopt compliance-driven quantum security models enhance their long-term cybersecurity resilience and regulatory readiness, ensuring that their manufacturing data, intellectual property, and supply chain transactions remain secure in a post-quantum threat landscape.21
AI-Driven Cyberrisk Assessment and Governance
AI is also transforming cyberrisk assessment and governance by providing predictive analytics, automated security decision making, and real-time cyberrisk intelligence. Traditional risk assessment models rely on manual security audits, periodic compliance evaluations, and reactive security responses, which are no longer effective against AI-powered cyberthreats, deepfake fraud, and ML-driven cyberattacks. AI-powered cybersecurity governance enables manufacturers to develop real-time risk assessment models, adaptive cybersecurity policies, and AI-automated compliance tracking systems.22
A 2025 study on AI-driven cyberrisk assessment frameworks found that organizations leveraging AI-based security analytics improved threat detection accuracy and reduced cyberrisk exposure.23 AI-driven cyberrisk modeling integrates ML algorithms that continuously monitor industrial network traffic. This integration allows organizations to better predict cyberattack patterns and identify vulnerabilities before attackers can exploit them. One of the most critical applications of AI in cybersecurity governance is the automation of cyberrisk scoring models. This application assigns risk scores to industrial assets, OT systems, and third-party vendor connections based on their exposure to cyberthreats, historical attack patterns, and real-time security telemetry. AI-powered cyberrisk scoring enables manufacturers to prioritize security investments. Manufacturers may also automate risk mitigation workflows and enforce dynamic security policies tailored to their operational risk profiles.
The integration of AI-driven compliance tracking systems further strengthens cybersecurity governance by ensuring that regulatory mandates, security policies, and cybersecurity investment strategies align with evolving threat landscapes. AI-powered compliance enforcement allows organizations to detect compliance violations in real time, automate security policy enforcement, and proactively address regulatory risk before it leads to financial penalties or security breaches.24
As AI-driven cybersecurity governance evolves, manufacturers must implement multilayered AI security frameworks to ensure that AI-based threat intelligence models remain resilient against adversarial AI attacks. These measures must also protect against data poisoning threats and algorithmic manipulation techniques. Organizations that integrate AI cybersecurity governance with zero trust enforcement and post-quantum cryptographic models are more likely to achieve long-term cybersecurity resilience.
Multi-Tiered Zero Trust Enforcement Strategies
Zero trust security architectures have become a fundamental cybersecurity governance model in industrial manufacturing, ensuring that no network entity, device, or user is implicitly trusted. Unlike perimeter-based security models, which rely on firewalls and static access control lists, zero trust architectures require continuous authentication, least-privilege access policies, and AI-driven identity verification to secure manufacturing assets, industrial networks, and cloud-based production management systems.25
Organizations that integrate AI cybersecurity governance with zero trust enforcement and post-quantum cryptographic models are more likely to achieve long-term cybersecurity resilience.Research conducted in 2024 on zero trust security implementation in industrial automation revealed that organizations implementing multi-tiered zero trust architectures experienced a 72% reduction in unauthorized access incidents and a 58% decrease in insider threats.26 These models integrate device-level authentication, user behavior analytics, and AI-powered anomaly detection to ensure that every access request undergoes continuous security validation before network privileges are granted. Additionally, zero trust models are particularly critical in supply chain cybersecurity governance. In these models, third-party vendors, remote maintenance providers, and cloud-based manufacturing partners interact with enterprise networks. AI-powered zero trust authentication systems are then used to analyze supplier risk profiles. Through these efforts, organizations enforce dynamic access controls and continuously monitor vendor security practices to prevent supply chain cyberthreats.
AI-driven zero trust enforcement strategies ensure that all manufacturing cybersecurity policies remain adaptive and responsive to evolving cyberrisk. The integration of AI-based zero trust access control with blockchain authentication and post-quantum encryption models enhances cybersecurity resilience across multi-tiered industrial environments.
Sustainable Cybersecurity Investment Strategies
Cybersecurity investments in manufacturing must be strategically aligned with business objectives, regulatory compliance mandates, and operational risk tolerance levels. Organizations must adopt sustainable cybersecurity investment strategies that ensure long-term cyberresilience, cost-effective security infrastructure deployment, and AI-driven security automation.27 Industry analyses suggest that dedicating a meaningful share of IT budget for cybersecurity can significantly reduce incident costs.28 Effective cybersecurity investment models prioritize proactive security automation, AI-driven threat detection infrastructure, and compliance-driven risk management frameworks to optimize long-term cybersecurity ROI.
Organizations can operationalize a proactive, ROI-focused cybersecurity investment model by coupling an enterprise-wide risk assessment with compliance mapping. This involves cataloging assets and threats in line with ISO/IEC 27005 risk analysis steps, then translating each high-probability, high-impact scenario into budget line items that reference the NIST SP 800-55 Rev. 1 Measurement Guide for Information Security, so leadership can quantify expected reductions in MTTD and mean time to recover (MTTR) per control deployed.29 To secure funding, board-level metrics should be presented that benchmark proposed controls against industry data, such as IBM Security’s finding that organizations with extensive automation and AI-driven analytics cut breach costs by an average of US$2.22 million, thereby linking every dollar requested to a defensible, evidence-based ROI target.30
Although smart manufacturing offers unprecedented efficiency and data-driven decision making, it also widens the attack surface of converged IT–OT environments, making the sector the most targeted industry for cyberattacks for four consecutive years.Zero trust cybersecurity investments lead to cost-effective security enforcement mechanisms, ensuring that organizations maximize security protection while minimizing unnecessary security expenditures. AI-driven cybersecurity risk modeling further enhances investment efficiency by identifying security gaps, prioritizing high-risk assets, and ensuring that cybersecurity budgets align with real-time risk exposure levels. Manufacturers must also consider the long-term financial impact of cyberresilience investments. These manufacturers have a responsibility to ensure that security automation, AI-driven risk assessment, and postquantum cryptographic frameworks remain cost-efficient and scalable. Organizations that integrate cybersecurity investments with AI-powered security automation and zero trust enforcement strategies enhance their overall security governance maturity.
Although smart manufacturing offers unprecedented efficiency and data-driven decision making, it also widens the attack surface of converged IT–OT environments, making the sector the most targeted industry for cyberattacks for four consecutive years.31 The World Economic Forum reports that manufacturing accounted for the highest share of global incidents in 2023, with a 15% year-over-year increase in attacks that blend ransomware with supply-chain compromise techniques.32 High-profile breaches demonstrate how a single plant-floor compromise can cascade into multimillion-dollar revenue losses and widespread supply chain delays. Regulators echoed these concerns, highlighting lateral movement from enterprise networks into legacy PLCs as a dominant path to sabotage safety-critical equipment.33 Compounding the danger, generative-AI-driven phishing kits and deepfake voice instructions reduce the skill threshold for social-engineering exploits that trick engineers into executing malicious commands.34 Finally, the global sprawl of firmware and software vendors introduces systemic risk. A single update that has been tampered with can propagate malware across thousands of interconnected devices, underscoring the need for rigorous supplier risk governance and authenticated update mechanisms in every Industry 4.0 deployment.35
Conclusion
A resilient cybersecurity posture in smart manufacturing rests on a layered‐defense fabric that unites governance frameworks, stringent risk analytics, and real-time monitoring into one coherent whole. Within that fabric, AI-driven threat intelligence, multi-tiered zero trust enforcement, and emerging post-quantum encryption serve as decisive differentiators that frustrate modern, AI-enhanced attackers. Compliance mandates, from NIST’s to ISA/IEC 62443, act as both catalyst and compass, turning best practice into a board-level obligation and funding priority. Manufacturers that translate these principles into day-one action will not only outmaneuver today’s adversaries but also future-proof their operations against the quantum and AI threats that loom on tomorrow’s horizon.
Endnotes
1 Senapati, B.; Rawal, B. S.; et al.; “Technology Innovations Model of Artificial Intelligence to Stop Industrial Espionage in Manufacturing Establishments,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, iss. 3, 2024
2 Gul, S.; “The Role of Industry 4.0 in Enhancing IT Security Strategies for Sustainable Corporate Growth,” ResearchGate, December 2024
3 National Institute of Standards and Technology, Framework for Improving Critical Infrastructure Cybersecurity, USA, 2018; International Society of Automation (ISA) and International Electrotechnical Commission (IEC), “ISA/IEC 62433 Series of Standards”
4 Hattali, A.; “Industry 4.0 and Cybersecurity: Enhancing Digital Transformation in Developed Economies,” ResearchGate
5 Chukwuemeka, C.; Oladosu, S.; et al.; “Redefining Zero-Trust Architecture in Cloud Networks: A Conceptual Shift Towards Granular, Dynamic Access Control and Policy Enforcement,” Magna Scientia Advanced Research and Reviews, vol. 2, iss. 1, 2021, p. 74-86
6 Akinsanya, A.; “Enhancing Process Efficiency and Security in the US Manufacturing Sector: Evidence from Industry Implementation,” IRE Journals, vol. 8, iss. 8, 2025
7 National Institute of Standards and Technology, Framework for Improving; MITRE, “MITRE ATLAS,” National Institute of Standards and Technology (NIST), NIST Risk Management Framework, USA
8 European Union Agency for Cybersecurity, AI Cybersecurity Best Practices for Critical Sectors, 2024
9 International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC), Joint Technical Committee on Information Technology (ISO/IEC JTC 1), ISO/IEC 27001:2022 Information security, cybersecurity and privacy protection—Information security management systems Although smart manufacturing offers unprecedented efficiency and data-driven decision making, it also widens the attack surface of converged IT–OT environments, making the sector the most targeted industry for cyberattacks for four consecutive years. Requirements, Edition 3, 2022, ; Reddy, S.; Lu, J.; et al.; “AI and Cybersecurity Challenges in the Manufacturing Sector,” October 2020
10 Biplob, M. B.; Marma, S. M.; et al.; “Securing Tomorrow's Digital World: Key Trends in Cybersecurity for 2024,” Preprints, 9 September 2024
11 Claroty, The Global State of Industrial Cybersecurity Report 2023, 2023
12 Fortinet, 2024 State of Operational Technology and Cybersecurity Report, 2024
13 Kim, Y.; Sohn, S. G.; et al.; “Exploring Effective Zero Trust Architecture for Defense Cybersecurity: A Study,” KSII Transactions on the Internet and Information Systems (TIIS), vol. 18, iss. 9, 2024, p. 2665-2691
14 Gupta, V.; Bharathi, S. V.; “Supply Chain Risk Management Through Zero-Trust Architecture,” Intelligent Systems for Smart Cities, 2024, p. 149-170
15 Rao, M.; Paul, B.; “Zero-Trust Model for the Smart Manufacturing Industry,” Applied Sciences, vol. 13, iss. 1, 2023, p. 221
16 National Institute of Standards and Technology, FIPS 203—Module-Lattice-Based Key- Encapsulation Mechanism Standard, USA, 2024 Kumar, M. S.; Harsha, B. K.; et al.; “AI-Driven Cybersecurity Modeling Using Quantum Computing for Mitigation of Attacks in IoT–SDN Network,” In Yadav, S.P.; Singh, R.; et al.; (Eds.), Quantum-Safe Cryptography Algorithms and Approaches: Impacts of Quantum Computing on Cybersecurity, p. 37–48, De Gruyter, Germany, 2023
17 Goldberg; Evans; “Conventional Cryptography Is Under Threat”
18 Daah, C.; Qureshi, A.; et al.; “Enhancing Zero-Trust Models in the Financial Industry Through Blockchain Integration: A Proposed Framework,” Electronics, vol. 13, iss. 5, 2024, p. 865
19 National Institute of Standards and Technology, FIPS 203
20 European Union Agency for Cybersecurity, AI Cybersecurity Best Practices
21 Shahzad, U.; Lu, C.; The Effect of Zero Trust Model on Organizations, Lund University, Sweden, 2023
22 Van Bossuyt, D. L.; Hale, B.; et al.; “Zero-Trust for the System Design Lifecycle,” Journal of Computing and Information Science in Engineering, vol. 23, iss. 6, 2023
23 Joshi, H.; “Emerging Technologies Driving Zero Trust Maturity Across Industries,” IEEE Open Journal of the Computer Society, vol. 6, 2025, p. 25-36
24 Russo, S.; “Industrial Demilitarized Zone and Zero-Trust Cybersecurity Models for Industrial Control Systems,” AMS Laurea Institutional Theses Repository,
25 Kolawole, I.; “Leveraging Cloud-Based AI and Zero-Trust Architecture to Enhance US Cybersecurity and Counteract Foreign Threats,” World Journal of Advanced Research and Reviews, vol.25, iss. 3, 2025
26 Schmitt, I. R. H.; Ortmann, M.; “Zero Trust Architectures for Interconnected Industry,” Fraunhofer, 2024
27 Ashfaq, S.; Patil, S. A.; et al.; “Zero Trust Security Paradigm: A Comprehensive Survey and Research Analysis,” Journal of Electrical Systems, vol. 19, iss. 2, 2023
28 IBM, Cost of a Data Breach 2024, 2024
29 International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC), ISO/IEC 27005:2022 Information security, cybersecurity and privacy protection—Guidance on managing information security risks, 2022; NIST, NIST SP 800-55 Vol. 1 Measurement Guide for Information Security: Volume 1—Identifying and Selecting Measures, USA, 2024
30 IBM, Cost of a Data Breach
31 IBM, “X-Force Threat Intelligence Index 2024 Reveals Stolen Credentials as Top Risk, With AI Attacks on the Horizon,” 21 February 2024
32 World Economic Forum, Global Cybersecurity Outlook 2024, 11 January 2024
33 European Union Agency for Cybersecurity (ENISA), ENISA Threat Landscape 2022, European Union, 2022
34 IBM, “X-Force Threat Intelligence”
35 World Economic Forum, Global Cybersecurity Outlook
Jarvis Tyrell Curry, CFE, CIA
Is an experienced information security and IT governance professional with extensive expertise in cybersecurity, risk management, and compliance frameworks. With a strong background in audit, risk assessment, and IT controls, Curry has played a key role in guiding organizations’ regulatory compliance and information security governance. His professional achievements include leading enterprisewide security programs, developing risk mitigation strategies, and advising on emerging threats. He has provided thought leadership in cybersecurity by aligning IT with business objectives to drive secure digital transformation.
