AI as the primary security concern
Artificial intelligence is now at the center of corporate security discussions. According to the 2026 Thales Data Threat Report, 70% of organizations see AI as their top data protection challenge. The reason is simple, AI has moved from handling small, specialized tasks to operating across entire digital environments. These systems now have access to large amounts of corporate data, from internal communications to customer records. When AI tools are granted this level of trust too quickly, they increase the possibility of data exposure or misuse.
The real risk isn’t that AI itself is malicious. The problem lies in how we integrate and govern it. When AI systems are plugged into enterprise infrastructure without proper oversight, they become an extension of the organization’s identity, a powerful but potentially uncontrolled one. As Sebastien Cano, Senior Vice President of Cybersecurity Products at Thales, said, “Insider risk is no longer just about people. It is also about automated systems that have been trusted too quickly.” This shift marks a new phase of cybersecurity, where the line between human and machine responsibility gets blurry.
For executives, the lesson is clear. Rapid adoption of AI must come with equally rapid investment in data governance and risk management. Strong access control policies, continuous monitoring, and clear accountability for how AI interacts with enterprise data are critical. Decisions about AI deployment should be strategic, not reactive. The future belongs to organizations that balance speed of innovation with thoughtful protection of their data assets.
Inadequate data visibility and inconsistent encryption
AI is only as secure as the data it can access. But most companies don’t have a full picture of where their data resides or how it’s classified. The same Thales Data Threat Report found that only 34% of organizations know where all their data is stored, and just 39% can accurately classify it. Without this clarity, enforcing protective measures across cloud and hybrid systems is inconsistent at best.
Another weak point is encryption. Nearly half, 47%—of all sensitive cloud data remains unencrypted. That means even if perimeter defenses are strong, sensitive information is still vulnerable once inside the network. Combined with limited visibility, this creates an environment where one misconfiguration or compromised credential can expose critical assets.
For leaders, this is no longer a back-office technical issue, it’s a strategic one. Data visibility and encryption must be part of the organization’s core operating model, not treated as optional security features. Companies that can trace and control their data flow across platforms gain more than security, they gain trust, efficiency, and regulatory confidence.
Executives should push for real-time data discovery tools and uniform encryption policies across all environments. These steps build both resilience and agility, allowing businesses to scale AI safely. In a world where AI and automation accelerate everything, clarity and control over data are what keep organizations stable and competitive.
Escalation of identity threats and credential attacks
Identity has become the new frontline of cybersecurity. According to the 2026 Thales Data Threat Report, 67% of organizations that faced cloud attacks said credential theft was the main technique used. As digital transformation expands, businesses now manage far more access points, not just human users but automated systems, APIs, and AI models that require their own credentials. Each one becomes a potential entry point for attackers.
The challenge intensifies as companies deploy more AI and automation tools. These systems rely on machine identities, keys, tokens, and secrets, to function. Without proper oversight, these credentials can be duplicated, shared, or exploited, magnifying the impact of a single breach. Half of the surveyed organizations noted secrets management as one of their highest security priorities. The sheer scale of non-human identities means traditional identity management systems are no longer enough.
For executives, understanding identity governance as a strategic priority, not just an IT function, is critical. This involves aligning teams around zero-trust principles, enforcing least-privilege access, and using continuous authentication mechanisms that adapt to behavior and context. When identity is secured, everything built on it gains stability. The companies that stay ahead will not only protect their systems but also preserve agility as automation continues to expand across business operations.
Rising threats from deepfakes and AI-Generated misinformation
AI is creating new forms of attack that blend digital manipulation and psychological tactics. The Thales Data Threat Report found that nearly 60% of companies have faced deepfake-driven incidents, and 48% have suffered reputational damage from AI-generated misinformation or impersonation. These attacks are not limited to the digital sphere, they target human perception and trust within organizations.
Deepfakes and synthetic media can be used to impersonate executives, manipulate employees, or mislead customers. Misinformation campaigns spread faster than ever, amplified by automated content generation. When these events occur, organizations must not only contain the technical breach but also manage the narrative, restore credibility, and clarify facts to their stakeholders. The acceleration of AI-driven communication means even small errors can spread widely before teams have time to respond.
Business leaders should integrate detection technology and verification processes into their communications infrastructure. Crisis response and information validation should evolve alongside cybersecurity defenses. This is as much about protecting brand trust as it is about protecting networks.
Executives must lead from the front, embedding ethics, transparency, and accountability into how their organizations use AI. The future of security will increasingly depend on rapid truth verification and coordinated response capabilities. Organizations that act decisively and uphold authenticity will retain resilience and credibility in an environment where digital deception continues to rise.
Security budget shortcomings and governance lag
AI adoption is accelerating faster than most organizations’ ability to secure it. The 2026 Thales Data Threat Report shows that while AI has become a central operational force, only 30% of companies have dedicated AI security budgets. More than half, 53%—still depend on traditional cybersecurity funding built for human users and perimeter-based systems. This imbalance leaves many teams applying outdated controls to environments driven by machine learning and automation.
Governance has not caught up either. Companies implement AI tools rapidly but often under policies designed for human interactions and older infrastructures. This gap allows AI systems to operate autonomously with excessive permissions and limited oversight. As these systems make more independent decisions, their ability to impact data integrity or compliance grows. Without targeted investment in governance and threat management, organizations risk being reactive rather than prepared.
Eric Hanselman, Chief Analyst at S&P Global 451 Research, said that “as AI becomes deeply embedded into enterprise operations, continuous data visibility and protection are no longer optional.” He’s right. Executives must treat AI security not as an addition to current frameworks but as a structural pillar of modern operations. Investing in dedicated AI security budgets is directly tied to business continuity and customer trust.
For leaders, the opportunity lies in shifting the security mindset from containment to foresight. This begins with funding that supports adaptive governance, cross-functional collaboration, and continuous monitoring. The organizations that make this adjustment early will move faster and face fewer disruptions as AI-driven operations expand. Those that delay will find it harder to maintain control as their systems scale beyond the reach of traditional defenses.
Key highlights
- AI has become the top security concern: AI’s rapid integration across enterprise systems has made it the leading data security risk. Leaders should tighten access controls and governance to prevent over-trusted AI systems from exposing sensitive data.
- Data visibility and encryption must improve: Most organizations lack full awareness of where their data resides or how it’s protected. Executives should invest in real-time data discovery and enforce consistent encryption across cloud and hybrid environments.
- Identity management is now mission-critical: Credential theft and weak machine identity governance are driving the majority of cloud security breaches. Leaders should strengthen identity access frameworks and adopt zero-trust architectures to safeguard both human and machine credentials.
- AI-driven misinformation demands faster response: Deepfakes and synthetic content are causing real financial and reputational damage. Executives must integrate detection tools, train teams for rapid incident response, and enhance communication verification processes.
- Security budgets must match AI’s pace: Only a minority of organizations have allocated dedicated AI security funding. Decision-makers should create AI-focused governance models and reallocate budgets toward dynamic, adaptive security programs built for automated systems.
