Key Takeaways
- 84 % of organizations suffered a material digital‑risk incident in the past year, yet only 7 % rate their digital‑risk programs as mature.
- Attackers now focus on trust, identity, and online reputation rather than solely on infrastructure.
- Executive and employee impersonation, look‑alike domains, and AI‑generated deception are the most prevalent tactics.
- Workforce protection remains limited, with most coverage confined to executives or high‑risk roles.
- AI‑generated deepfakes and synthetic media undermine traditional fraud indicators, making detection a top investment priority.
- Deployment of AI agents creates new attack surfaces via indirect prompt‑injection, but visibility and automated controls are scarce.
- An “AI Trust Gap” persists because few organizations can automatically detect and contain compromised agents in real time.
- Detection and response still rely heavily on external notifications; continuous monitoring, automated alerting, and end‑to‑end coverage are uncommon.
- Ownership of digital‑risk efforts is fragmented across security, fraud, communications, legal, and executive‑protection teams, slowing response.
- Spending is rising, but tools remain disconnected; a shift toward integrated, agentic response models is needed to operate at AI‑era speed.
Digital Risk Landscape Expands Beyond Traditional Security
The 2026 Digital Risk Report shows that modern threat actors have moved past attacks on endpoints, networks, and cloud workloads. Instead, they target the public internet where brands, executives, employees, customers, and business processes reside. By exploiting trust, identity, and online reputation, adversaries can inflict damage that spreads across multiple business functions. The report finds that 84 % of respondents experienced a material digital‑risk incident in the last year, underscoring how pervasive these threats have become despite heavy investments in traditional security controls.
Executive and Employee Identities as Primary Targets
People have become one of the most exposed attack surfaces. More than half of surveyed organizations reported incidents involving executive or employee impersonation over the past twelve months. Attackers gather personal data from public sources, data brokers, and credential leaks to craft convincing spoofed communications on social media, professional networks, and email. These tactics leverage authority and familiarity to deceive targets, yet many firms lack comprehensive person‑of‑interest monitoring, threat profiling, or personal‑information removal programs, leaving a significant visibility gap.
Workforce Protection Remains Limited
While executive protection programs have gained traction, broader workforce coverage lags considerably. Most organizations either protect only executives, extend limited coverage to select high‑risk roles, or operate without a formal workforce‑protection strategy altogether. Attackers frequently target employees who hold privileged access to financial systems, administrative controls, customer relationships, or critical business processes—individuals who may not be senior executives. Consequently, narrow protection strategies leave key personnel exposed to impersonation and credential‑theft campaigns.
AI‑Generated Deception and Detection Challenges
Artificial intelligence is reshaping both offensive and defensive operations. Nearly half of respondents confirmed or suspected synthetic‑media impersonation incidents involving deepfake videos, cloned voices, or other AI‑generated content that mimicked executives or brand representatives. Traditional fraud indicators—poor grammar, unnatural imagery, inconsistent messaging—are losing reliability as generative AI tools improve. Organizations now view AI‑generated deception detection as a strategic priority, calling for earlier interception of campaign infrastructure before fraudulent content reaches its intended audience.
AI Agents Introduce a New Security Boundary
Beyond external threats, the report highlights risks tied to AI agents and automated workflows that interact with external data. Many firms deploy AI‑powered systems for communications, research, transactions, and decision‑making, yet visibility and control over these agents remain limited. Indirect prompt‑injection attacks—where malicious instructions are hidden in external content consumed by AI agents—are emerging as a growing concern. If successful, manipulated agents can execute unintended actions without human awareness, but only a small percentage of organizations report comprehensive visibility and active controls governing these external interactions.
AI Trust Gap and Governance Needs
The study identifies an “AI Trust Gap” driven by the inability of most organizations to automatically detect and contain compromised AI agents before harmful actions occur. While some have instituted manual reviews or limited detection capabilities, very few have automated containment mechanisms capable of stopping malicious agents in real time. Because AI agents operate at machine speed, reliance on human review allows damaging actions to unfold before intervention. Researchers argue that AI governance must be afforded the same operational rigor applied to identity and access‑management programs to close this gap.
Detection, Response, and Attribution Shortcomings
Many organizations remain heavily dependent on external parties—customers, partners, or the public—to discover digital‑risk incidents. Continuous monitoring, automated alerting, and structured triage processes are uncommon across the market. Attribution and adversary‑tracking efforts also fall short; firms often focus on removing individual malicious artifacts (fake accounts, fraudulent domains) without investigating the broader infrastructure and operators behind the activity. This reactive stance allows threat actors to sustain campaigns longer and adapt faster than defenders can respond.
Fragmented Ownership and Investment Trends
Digital‑risk ownership is frequently dispersed among security operations, threat intelligence, fraud teams, communications, legal, and executive‑protection functions. In many organizations, no single team holds end‑to‑end responsibility, resulting in inconsistent coordination, delayed responses, and incomplete visibility into adversary campaigns. Despite plans to increase digital‑risk spending over the next year, most continue to rely on disconnected tools, manual processes, and fragmented workflows. Purpose‑built digital‑risk protection platforms remain the exception, and simply raising budgets without improving operational integration risks perpetuating the very fragmentation organizations seek to eliminate.
Toward an Agentic Response Model
The report concludes that digital‑risk programs must evolve from reactive operations to agentic response models capable of operating at AI‑era speed. Such models would tightly integrate detection, investigation, attribution, remediation, verification, and continuous learning into a single operational loop. AI agents would automate repetitive response functions—such as artifact enrichment, initial triage, and basic remediation—while human teams retain oversight, governance, escalation authority, and strategic decision‑making responsibilities. By adopting this approach, organizations can better protect digital trust, limit business disruption, and respond effectively to increasingly sophisticated online threats over the next 12‑24 months.