Key Insights from ISACA’s 2026 Tech Trends Report for Government Risk Management

The landscape of government cybersecurity has reached a critical inflection point. According to ISACA’s 2026 Tech Trends and Priorities Pulse Poll, which surveyed 2,963 digital trust professionals across cybersecurity, IT audit, governance, risk, and compliance fields, federal and state agencies face an unprecedented convergence of threats, regulatory mandates, and technological complexity.

The findings reveal stark realities for government risk management professionals. Fifty-nine percent of respondents identify AI-driven cyber threats and deepfakes as their primary concern for 2026—marking the first time artificial intelligence threats have surpassed traditional attack vectors. Simultaneously, 66% cite regulatory compliance as their organization’s top focus area, while 62% prioritize business continuity and resilience. Perhaps most concerning: only 13% of professionals feel “very prepared” to manage generative AI risks.

These statistics matter because government agencies handle the nation’s most sensitive data—from classified intelligence to citizen personally identifiable information (PII). As agencies accelerate digital transformation and AI adoption under mandates like Executive Order 14028 and the DoD’s Zero Trust strategy, the intersection of data security, compliance, and privacy demands comprehensive solutions that address both traditional and emerging threats.

Key Takeaways

1. AI-Driven Threats Now Dominate the Cybersecurity Landscape. Sixty-three percent of professionals cite AI-driven social engineering as the most significant cyber threat for 2026, surpassing traditional concerns like ransomware. Government agencies face sophisticated AI-powered phishing, deepfakes, and automated reconnaissance at unprecedented scale.
2. A Critical Preparedness Gap Exists for AI Risk Management. Only 13% of professionals feel very prepared to manage generative AI risks despite 59% identifying AI threats as their primary concern. This disconnect creates vulnerability as agencies accelerate AI adoption under federal mandates.
3. Regulatory Compliance Demands Immediate Action with Tight Deadlines. CMMC 2.0 becomes mandatory for DoD contracts by October 2026, requiring 12-18 months for certification. Agencies must simultaneously implement Zero Trust Architecture by 2027 while navigating evolving AI-specific regulatory frameworks.
4. Zero Trust Implementation Requires Data-Centric Security Architecture. Effective Zero Trust extends beyond network controls to content-defined approaches protecting data based on classification. AI systems require attribute-based access controls, secure data gateways, and comprehensive audit trails throughout the AI lifecycle.
5. Workforce Development Is Critical to Managing Emerging Threats. Forty-one percent cite keeping pace with AI-driven change as their biggest professional concern. Agencies must invest in upskilling current staff on AI security, developing cross-functional AI governance expertise, and competing for scarce AI security talent.

Evolving Threat Landscape: AI as Adversary

AI-Driven Threats Dominate Professional Concerns

The ISACA survey identifies a fundamental shift in the threat environment. Sixty-three percent of respondents cite AI-driven social engineering as the most significant cyber threat facing organizations in 2026—the highest response in the poll. This represents a notable change from previous years when ransomware dominated professional concerns. While ransomware still ranks high at 54%, and supply chain attacks concern 35% of respondents, AI-powered threats have emerged as the primary focus.

The implications for government operations are substantial. AI-driven social engineering can craft highly personalized phishing campaigns targeting federal employees, generate convincing deepfakes to impersonate officials, and automate reconnaissance of government networks at unprecedented scale. State and local agencies, which often operate with limited cybersecurity resources, face vulnerability to these sophisticated attacks.

Preparedness Gap

The data reveals a troubling disconnect between threat awareness and organizational readiness. While 59% of professionals expect AI-driven threats to keep them awake at night, actual preparedness lags significantly. Only 13% describe their organizations as “very prepared” to manage generative AI risks. Another 30% admit they are not very prepared or not prepared at all.

This preparedness gap manifests in workforce strain. Forty-one percent of respondents identify keeping pace with AI-driven change as their biggest professional concern going into 2026, followed by the increasing complexity of threats at 27%. This challenge compounds existing workforce issues, with 23% citing talent retention and hiring difficulties, and 14% reporting burnout among cybersecurity staff.

AI-Specific Data Risks for Government Operations

The integration of AI into government operations introduces distinct data security challenges beyond traditional threat models. Unauthorized AI systems accessing Controlled Unclassified Information (CUI) or classified data represents a significant risk. Unlike human users, AI systems can consume and process vast quantities of data at machine speed, making traditional access controls insufficient.

Training data protection presents another critical concern. Government agencies developing AI capabilities must secure the enterprise data used for model training. Data poisoning—the injection of malicious or misleading information into training datasets—can compromise AI model integrity. This risk extends beyond external actors to include insider threats and supply chain vulnerabilities in AI development processes.

Production AI systems create ongoing exposure through inference and decision-making operations. Prompt injection attacks can manipulate AI systems to bypass security controls or extract sensitive information. Government AI systems making high-stakes decisions about benefits eligibility, security clearances, or resource allocation require controlled data access that maintains appropriate security classifications throughout the AI lifecycle.

To address these challenges, government agencies need zero-trust AI data access controls that verify every access request, granular permissions ensuring only authorized AI systems access specific datasets, and real-time access tracking providing complete visibility into AI data consumption. Secure AI data gateways create protected pathways between AI systems and enterprise data repositories while maintaining comprehensive audit trails for compliance and incident investigation.

Regulatory Compliance: The Top Priority

Compliance as Strategic Imperative

Regulatory compliance tops organizational priorities for 2026, with 66% of ISACA respondents identifying it as a very important focus area. This prioritization reflects the expanding scope and increasing complexity of government cybersecurity mandates. Thirty-two percent expect regulatory complexity and global compliance risks to keep them awake at night—a significant concern in an environment where non-compliance can result in contract disqualification, financial penalties, and mission impact.

The data also reveals an evolving perspective on regulatory requirements. Rather than viewing compliance purely as burden, 62% of respondents believe cyber-related regulation will drive business growth, and 78% think it will advance digital trust over the next few years. This shift suggests recognition that compliance frameworks, when properly implemented, strengthen security posture and enhance organizational capability.

Zero Trust Architecture Mandate

Executive Order 14028, “Improving the Nation’s Cybersecurity,” mandates federal agencies adopt Zero Trust Architecture (ZTA) principles. The Department of Defense Zero Trust Strategy sets an adoption target of fiscal year 2027 for achieving initial capabilities across the defense enterprise. CISA’s microsegmentation guidance provides practical implementation strategies for preventing lateral network movement—a key Zero Trust principle.

Zero Trust represents a fundamental shift from perimeter-based security to continuous verification. The model assumes no implicit trust based on network location, requiring verification for every access request regardless of source. Data-centric security forms the foundation of effective Zero Trust implementation, requiring understanding of data flows, implementing granular access controls, and maintaining comprehensive visibility into data interactions.

For AI systems, Zero Trust implementation requires attribute-based access controls (ABAC) that dynamically adjust permissions based on data sensitivity, AI system requirements, and contextual factors. This approach replaces binary access decisions with risk-based evaluations considering multiple variables. Agencies need secure conduits between AI systems and data repositories, real-time monitoring of AI data consumption, and automated policy enforcement applying governance rules as AI systems request data access.

CMMC 2.0 Compliance Requirements

The Cybersecurity Maturity Model Certification (CMMC) 2.0 program establishes mandatory cybersecurity standards for the Defense Industrial Base. The final DFARS rule, published September 10, 2025, takes effect November 10, 2025. By October 31, 2026, CMMC compliance becomes mandatory for all new DoD contract awards.

CMMC establishes three certification levels:

• Level 1 (Foundational) for Federal Contract Information requiring 17 basic practices and self-assessment
• Level 2 (Advanced) for CUI requiring all 110 practices from NIST SP 800-171 with third-party assessment for high-priority contracts
• Level 3 (Expert) for critical national security information assessed by the Defense Industrial Base Cybersecurity Assessment Center

Given that achieving CMMC Level 2 certification typically requires 12-18 months, government contractors and agencies managing contractor relationships must begin preparation immediately to maintain eligibility for defense contracts.

AI-Specific Regulatory Frameworks

Government AI adoption accelerates under multiple mandates, but regulatory frameworks for AI security continue evolving. The NIST AI Risk Management Framework (AI RMF 1.0) provides comprehensive guidance for identifying, assessing, and managing AI risks, addressing trustworthiness characteristics including validity, reliability, safety, security, resilience, accountability, transparency, and privacy enhancement.

Executive Order 14110 establishes federal AI safety and security requirements, directing agencies to implement safeguards for AI systems, maintain data inventories for AI usage, and align with NIST standards. Government agencies must track which AI systems access what data, document training data sources, and maintain comprehensive audit trails for AI interactions.

Supporting these requirements demands technical capabilities including comprehensive audit trails documenting data sources used for AI training and inference, data provenance tracking enabling identification of training data throughout AI lifecycles, information integrity management ensuring accuracy and suitability of training data, and immutable logging creating tamper-proof records of all AI data interactions.

Data Privacy and Sovereignty

Thirty percent of ISACA respondents identify data privacy and sovereignty as technology priorities impacting their work in 2026. Government agencies manage complex privacy obligations under sector-specific regulations including the Privacy Act of 1974, HIPAA for health information, and state privacy laws like the California Consumer Privacy Act.

Cross-border data transfer requirements complicate international government operations. Data localization mandates in various jurisdictions require certain data categories remain within specific geographic boundaries. Agencies conducting multinational operations must implement technical controls ensuring data residency compliance, often requiring geographic-specific infrastructure and routing rules. For AI systems, this extends to controlling where AI training occurs and where inference processing happens to maintain data sovereignty.

Data Security Fundamentals for Government Operations

Content-Defined Zero Trust Approach

Effective Zero Trust implementation for government operations requires moving beyond network-centric security to content-defined approaches that protect data based on classification, sensitivity, and business context. Government agencies manage diverse data types requiring differentiated protection: Federal Contract Information requires basic safeguarding, CUI demands 110 NIST SP 800-171 security requirements, and classified information falls under Intelligence Community directives.

Data categorization forms the foundation of this approach. Agencies must identify what data they possess, classify it according to sensitivity and regulatory requirements, and document where it resides and how it flows through systems. This inventory process enables implementation of granular access controls and demonstrates regulatory compliance.

Real-time access tracking provides visibility essential for Zero Trust validation. Agencies need comprehensive logs showing who accessed what data, when, from where, and what actions they performed. This visibility enables anomaly detection, supports incident investigation, and provides evidence for compliance audits.

Technical Security Controls

Government-grade data security requires technical controls that address both confidentiality and integrity. Double encryption—implementing encryption at both file level and disk level—protects data through multiple key management domains. Transport Layer Security (TLS) 1.3 protects data in transit, while AES-256 encryption secures data at rest. Government agencies should implement FIPS 140-2 validated cryptographic modules to ensure encryption implementations meet federal standards.

End-to-end encryption ensures data protection throughout its lifecycle. Customer-owned encryption keys give agencies complete control over data access without depending on third-party key escrow, addressing concerns about cloud service provider access and providing assurance that only authorized agency personnel can decrypt sensitive information.

AI Data Security Gateway Architecture

Securing AI system access to government data requires purpose-built architecture addressing unique AI workflow requirements. A secure AI data gateway creates a protected pathway between AI systems and enterprise data repositories, mediating all interactions, enforcing security policies, and maintaining comprehensive audit trails.

API-first integration ensures seamless connection with existing AI infrastructure. Government agencies deploying machine learning platforms, data science environments, or AI-powered applications can integrate gateway capabilities without requiring fundamental architecture changes. Retrieval-Augmented Generation (RAG) support enables secure data enhancement for large language models, providing a method to leverage internal knowledge bases while maintaining granular control over information access.

Advanced data protection layers address specific AI security challenges. Data watermarking embeds identifying information in datasets, enabling agencies to track data usage across AI systems and detect unauthorized data exfiltration. Automated data loss prevention (DLP) scanning prevents inappropriate AI access to sensitive information, implementing policies that block AI systems from consuming datasets containing classified information, PII, or other sensitive categories unless explicitly authorized.

Government Use Cases

Government agencies require secure data communications across diverse scenarios including diplomatic correspondence protection, budgetary and financial data distribution, policy development collaboration, cybersecurity threat intelligence sharing between agencies, grant application exchange, classified information transfer, and interagency communications.

For AI-specific scenarios, agencies need capabilities for secure AI model training with government data, enabling AI capability development while protecting sensitive information used for training, and controlled AI inference for mission-critical decisions, providing real-time data access with appropriate security controls for AI systems supporting benefits eligibility determinations, security clearance processing, or resource allocation.

Data Governance and Privacy Management

Comprehensive Governance Framework

Data governance encompasses four interconnected components: data residency addressing geographic location requirements, security controls protecting confidentiality and integrity, privacy protections addressing personal information handling, and compliance demonstrating adherence to regulatory frameworks.

The Data Protection Officer role provides dedicated governance leadership, coordinating privacy program activities, advising on data protection obligations, serving as regulatory contact point, and monitoring compliance. Data categorization and tagging protocols enable automated security control application based on sensitivity, requiring consistent classification schemes, metadata tagging at data creation, and maintenance of classifications throughout data lifecycles.

AI-Enhanced Governance Capabilities

AI introduces new governance requirements while enabling enhanced governance capabilities. Comprehensive audit trails for AI data usage document which data sources AI systems use for training and inference, supporting regulatory compliance and enabling algorithmic transparency. System-level activity logs capture granular details including prompt inputs, data retrieved for context, model decisions, confidence scores, and final outputs.

Data provenance tracking traces information lineage throughout AI lifecycles, enabling agencies to know which documents, databases, or systems contributed to training datasets, how data was preprocessed, and what versions models used for specific outputs. Policy enforcement frameworks apply governance rules to AI system data consumption through risk policies defining which AI systems can access specific data categories, what processing operations are permitted, and what review processes apply.

Real-time Security Information and Event Management (SIEM) integration enables immediate analysis of security events, providing critical visibility into AI system behavior. Security analysts can monitor which AI systems access sensitive data, identify unusual access patterns, and detect potential data exfiltration attempts.

Privacy by Design Principles

Privacy by design integrates data protection into systems development from initial concept through deployment. The European Union’s data protection framework identifies seven key principles:

1. Accountability
2. Accuracy
3. Integrity and confidentiality
4. Purpose limitation
5. Data minimization
6. Storage limitation
7. Lawfulness, fairness, and transparency

Application to AI system development proves particularly important given AI’s data-intensive nature and potential for privacy impacts.

Monitoring and Reporting

Comprehensive dashboards provide security and compliance teams with centralized visibility into data protection posture, displaying key metrics including access attempt volumes, policy violations, user activity patterns, and compliance status. For AI-specific monitoring, dashboards should show which AI systems consume what data types, access frequency and volumes, and policy exceptions requiring review.

Compliance-specific reporting automates evidence generation for regulatory audits. Rather than manually compiling evidence from multiple systems, automated reporting extracts relevant data, formats it according to auditor requirements, and maintains records demonstrating continuous compliance. Government agencies managing multiple regulatory frameworks benefit from reporting capabilities generating framework-specific outputs—FISMA reports, CMMC evidence packages, privacy program assessments—from unified data sources.

Business Continuity and Resilience

Continuity as Priority

Sixty-two percent of ISACA respondents cite business continuity and resilience as very important organizational focus areas for 2026. Critical government services—emergency response, benefits delivery, law enforcement, healthcare, education—require continuous availability. Extended outages create public safety risks, harm citizens depending on services, and undermine confidence in government capability.

Maintaining operations during AI-driven attacks presents new continuity challenges. AI-powered attacks can identify vulnerabilities, adapt tactics, and operate at machine speed, potentially overwhelming traditional security operations center response capabilities. Agencies need automated defense capabilities, resilient architectures that continue functioning despite partial compromise, and procedures for operating in degraded modes during sustained attacks.

Incident Response and Recovery

Comprehensive incident response plans define roles, procedures, and communication protocols for managing security incidents from initial detection through recovery. Government agencies should develop plans addressing diverse incident types—ransomware, data breaches, insider threats, supply chain compromises, AI system manipulation—with specific playbooks for each scenario.

AI-specific incident response addresses unique scenarios including model poisoning, prompt injection attacks, and adversarial examples causing AI system failures. Agencies deploying AI for operational decisions should develop procedures for validating AI outputs when manipulation is suspected, rebuilding models from verified data sources, and operating without AI capabilities during remediation efforts.

Resilience Through Data Protection

Comprehensive data protection contributes to operational resilience by reducing single points of failure and enabling rapid recovery. Geographic redundancy for data storage ensures availability despite localized incidents. Continuous compliance monitoring provides ongoing assurance rather than point-in-time assessment snapshots, checking control implementation, validating configuration settings, and providing real-time compliance status.

Secure AI data gateways ensure AI capabilities continue functioning even if surrounding networks face compromise, enabling agencies to maintain AI-powered operations during extended incident response and recovery processes. Platforms designed to adapt to evolving AI regulations enable agencies to maintain compliance as requirements change through configuration updates rather than fundamental redesigns.

Workforce and Talent Management

Sixty-two percent of ISACA respondents indicate their organizations plan to hire for digital trust roles in 2026, but 44% of those with hiring plans expect difficulty filling positions with qualified candidates. Government agencies face particular challenges competing for cybersecurity talent against private sector organizations offering higher compensation. The growing need for AI security specialists—professionals with expertise in both AI and cybersecurity—compounds talent challenges.

Thirty-nine percent of respondents prioritize workforce upskilling in data security as very important. AI safety and security training enables current cybersecurity professionals to extend expertise to AI-specific threats and controls, covering AI attack vectors, security controls for AI systems, AI-specific regulations and frameworks, and operational procedures for securing AI workloads.

Understanding AI risk frameworks including NIST AI RMF enables security professionals to assess AI system risks, implement appropriate controls, and communicate AI risks to leadership. Continuous learning and certification programs demonstrate professional competency and provide structured skill development pathways through certifications like CISSP, CISM, and newer AI-focused credentials.

Actionable Recommendations for Government Agencies

Based on ISACA’s 2026 Tech Trends findings, agencies should prioritize five critical action areas:

1. Establish Robust AI Governance and Risk Frameworks

   Government agencies must move beyond ad hoc approaches through structured governance programs aligned with NIST AI RMF and federal AI requirements. Deploy zero-trust AI data access controls that verify every AI system access request, limit access to minimum necessary data, and maintain comprehensive audit trails. Create AI-specific data protection policies addressing which data classifications AI systems can access, what processing operations are permitted, and what monitoring requirements govern AI operations.

2. Accelerate Workforce Upskilling and Talent Pipeline Development

   Invest in developing current workforce AI security capabilities through training programs covering AI attack vectors, AI system security controls, and AI-specific regulations. Continuous learning keeps security teams current as AI technology and threats evolve. Building AI governance expertise requires cross-functional development including security professionals, privacy officers, legal counsel, and program managers.

3. Modernize Legacy Systems and Infrastructure

   Legacy system modernization reduces vulnerabilities and enables integration with modern security tools. Deploy secure AI data gateway architecture providing protected pathways between AI systems and enterprise data. Implement double encryption and advanced controls including customer-owned encryption keys, FIPS 140-2 validated cryptography, multi-factor authentication, and automated data loss prevention.

4. Strengthen Cyber Resilience and Business Continuity Planning

   Prepare for sustained AI-driven attacks through AI-specific incident response procedures addressing model poisoning scenarios, prompt injection attacks, and adversarial examples. Testing plans through exercises identifies vulnerabilities before adversaries exploit them. Establish redundancy for AI-dependent operations ensuring agencies can maintain critical functions if AI systems require disablement during security incidents.

5. Prepare for Regulatory Complexity and International Compliance

   Navigate expanding regulatory requirements through multi-framework compliance automation reducing burden through tools that map security controls to multiple requirements and generate framework-specific compliance reports. Monitor evolving AI regulations to enable proactive compliance. Implement cross-border data protection capabilities addressing data sovereignty and international compliance requirements.

Immediate Priority Actions

• Conduct AI data risk assessment identifying which AI systems your agency operates, what data those systems access, and what risks AI processing creates.
• Implement zero-trust AI access controls as immediate risk mitigation—requiring authentication, limiting access to minimum necessary data, and logging AI data consumption.
• Deploy comprehensive audit trails for AI usage enabling monitoring and investigation.
• Establish data provenance tracking documenting sources of AI training data.
• Engage with AI security platforms designed for government requirements, providing FedRAMP authorization, understanding of FCI and CUI requirements, and vendor experience with government security needs.

Conclusion

The convergence of data security, compliance, and privacy challenges identified in ISACA’s 2026 Tech Trends report demands integrated approaches from government risk management professionals. With 59% concerned about AI threats but only 13% feeling prepared, the urgency for action is clear. Government agencies cannot treat security, compliance, and privacy as separate initiatives—they form interconnected pillars requiring coordinated strategy, unified architecture, and comprehensive governance.

AI adds new dimensions to traditional security frameworks. While government agencies have decades of experience protecting data from human threats, AI introduces machine-speed data consumption, automated attack capabilities, and processing at scales exceeding human oversight capacity. Traditional security controls designed for human users prove insufficient for AI systems requiring continuous data access.

Data-centric security provides the foundation for addressing all three pillars. By implementing security controls that follow data regardless of network location or processing system, agencies protect information throughout its lifecycle. Secure AI data gateways enable agencies to leverage AI capabilities while maintaining control, visibility, and compliance over sensitive data throughout the AI lifecycle—from training through inference and output generation.

Meeting 2026-2027 compliance deadlines for CMMC and Zero Trust requires immediate action given 12–18-month implementation timelines. Government agencies must balance enabling innovation with protecting sensitive data through purpose-built security architectures. Building sustainable security programs supporting AI adoption requires investment in people, process, and technology—workforce development, AI governance frameworks, and secure AI data gateway infrastructure.

The path forward requires assessment of current AI data security posture, strategic planning for compliance deadlines, evaluation of purpose-built solutions addressing government-specific requirements, and proactive approaches to AI risk management. Government agencies that take decisive action now will be positioned to leverage AI capabilities securely while meeting regulatory requirements.