Cyber, AI, and Third-Party Risks Top the Executive Agenda for 2026-2028

Executive Risk Outlook: Cyber, Third-Party Exposure, and AI Lead 2026-2028 Priorities

A new global survey of 1,540 board members and C-suite leaders from the NC State ERM Initiative and Protiviti sets a clear agenda. Near-term risk is dominated by cybersecurity, third-party exposure, tech debt, and AI implementation pressure. The same leaders also flagged customer disruption, security/privacy, and AI deployments as the biggest decade-out concerns.

Top 10 Near-Term Risks (2026-2028)

Cyber threats (Operational)
Third-party risks (Operational)
Adoption of emerging technologies requiring workforce upskilling (Strategic)
Legacy IT infrastructure performance gaps (Operational)
Economic conditions, including inflation (Macroeconomic)
AI implementation risks (Operational)
Talent acquisition and retention challenges (Operational)
Regulatory uncertainty and fragmentation (Strategic)
Labor availability (Macroeconomic)
Global market and trade policy changes (Macroeconomic)

Five of the top risks are operational, signaling a push for internal resilience and reliable execution. Macroeconomic pressure shows up in labor availability, inflation, and trade policy. Strategic friction points-workforce transformation for new tech and regulatory fragmentation-carry outsized impact.

What Different Leaders Rank Highest

Board Members: Cyber threats; skills and talent acquisition; talent and labor availability
CEOs: Talent and labor availability; skills and talent acquisition
CFOs: Cyber threats; third-party risks; legacy IT and operations
CIOs/CTOs: Cyber threats; third-party risks; legacy IT and operations
CHROs: Talent and labor availability; skills and talent acquisition; increases in labor costs

Translation: cyber, talent, and tech debt are shared problems. Align your board agenda, capital plan, and operating model around those three pillars.

A Decade Out: Where Leaders Expect the Biggest Shifts

Customers and competition: No. 1 by 17%; Top 3 by 42%
Security and privacy: No. 1 by 17%; Top 3 by 40%
AI deployments: No. 1 by 13%; Top 3 by 39%
Markets and economies: No. 1 by 13%; Top 3 by 36%
Talent challenges: No. 1 by 9%; Top 3 by 32%

The long view is customer disruption, security/privacy, and scaled AI. Markets and talent still matter, but the competitive edge will come from how well you serve customers with secure, trustworthy AI-enabled experiences.

AI: Value Is Clear, Governance Is the Gate

Data risks and cybersecurity exposure - 31%
Integrating AI with existing tech, processes, and workforce - 31%
Equipping the workforce to realize AI's value - 29%
Inability to deploy AI at a competitive pace - 28%
Lack of governance and accountability for AI deployments - 24%

Set an AI control framework early: data access and lineage, model inventory, risk classification, human-in-the-loop controls, and post-deployment monitoring. The NIST AI Risk Management Framework is a practical starting point for policy, roles, and metrics.

Growth Is Still on the Table

Nearly 7 in 10 organizations are optimistic about short-term revenue growth. 62% expect to expand strategic alliances and partnerships, and 52% see upside in foreign markets.

That mix calls for smart ecosystem plays: co-sell partnerships, data-sharing agreements with clear risk controls, and regional go-to-market experiments with rapid feedback loops.

Where Investment Dollars Are Going

Cybersecurity: 15% No. 1; 43% Top 3
Business process improvements: 13% No. 1; 35% Top 3
Infrastructure modernization: 14% No. 1; 33% Top 3
Data privacy: 13% No. 1; 29% Top 3
Customer experience: 14% No. 1; 27% Top 3

This signals a practical playbook: reduce tech debt, harden controls, clean up data, and fund the customer journey where AI can produce measurable gains.

Boardroom Action Plan for the Next 12-18 Months

Cyber and third-party defense: Validate incident response, adopt zero-trust principles, test backups, and tighten vendor risk reviews. Consider CISA's Secure by Design guidance for procurement and architecture decisions.
AI governance: Approve enterprise policy, define RACI, set model risk tiers, require red-teaming for sensitive use cases, and track model performance post-release.
Upskilling at scale: Map critical roles, set AI and data skill baselines, and fund applied training tied to live projects. For structured programs by job role, see Complete AI Training.
Modernize core infrastructure: Prioritize systems that bottleneck security, data reliability, and AI integration. Time-box migrations and retire legacy components decisively.
Ecosystem discipline: Expand alliances, but bake in clear SLAs, shared controls, and exit plans. Score partners on security and operational maturity.
Macro and labor resilience: Run scenarios on inflation, labor scarcity, and trade volatility. Pre-plan cost levers, pricing moves, and supply shifts.
Metrics and reporting: Standardize dashboards for cyber posture, AI impact, tech debt burn-down, and talent pipeline health.

Bottom Line

The signal is consistent: protect the core, fix tech debt, and deploy AI with discipline. Leaders who do this while building smart partnerships will capture the upside without exposing the enterprise to avoidable risk.