AI Transformation and the New Front Line of Cybersecurity: Why the Endpoint Matters More Than Ever
Insights from Vincent Lee, Chief Growth Officer, AhnLab
Kenny Yeo, Frost & Sullivan Director, Head of Asia Pacific Cybersecurity Practice
AI Is Not Just Another Technology Upgrade
Artificial intelligence is rapidly transforming how organizations operate—but it is also fundamentally reshaping how cyber risk is created, amplified, and managed. Unlike previous technology waves, AI does not simply introduce new tools into the enterprise; it changes how decisions are made, how data is consumed, and where critical actions take place.
In a recent discussion with Vincent Lee, Chief Growth Officer (CGO) and Senior Vice President at AhnLab, a clear message emerged: AI is not merely augmenting existing workflows—it is changing the structure of work itself, and cybersecurity strategies must evolve in step.
“AI is no longer just introducing new security features. It is fundamentally changing how work is done, how data flows, and where decisions are made—and security has to evolve with that reality.”
Vincent Lee, CGO, AhnLab
This shift has significant implications for security leaders navigating AI-driven transformation, particularly as AI usage increasingly moves closer to end users and operational environments.
When AI Accelerates Both Attackers and Defenders
AI is delivering asymmetric advantages across the cyber landscape. On one hand, attackers are rapidly exploiting AI to scale phishing, social engineering, malware variation, and reconnaissance—dramatically reducing the cost and effort of launching sophisticated attacks. On the other hand, defenders are deploying AI to improve threat detection, prioritize alerts, and alleviate persistent talent shortages in security operations centers (SOCs).
However, these benefits do not materialize evenly. Attackers can apply AI almost immediately to improve productivity. Defenders, by contrast, must first transform governance models, data controls, and operational processes in order to use AI safely.
Vincent highlights this imbalance succinctly:
“Attackers gain value from AI almost immediately, while defenders need to transform governance, data control, and operational processes before AI can be used safely. That’s why attackers often see benefits earlier in the cycle.”
This timing gap is most evident at the endpoint and edge, where AI copilots, browser-based generative AI, and local inference engines place AI capabilities directly into the hands of users.
The Endpoint Becomes the New AI Decision Interface
Traditionally, endpoints were treated primarily as execution environments—for files, emails, and applications. In AI-enabled organizations, the endpoint has evolved into a decision interface, where users input data into AI systems, consume AI-generated outputs, and act on those outputs in real time.
This shift introduces a new category of risk:
- Sensitive data entered directly into AI prompts
- Prompt injection or indirect instruction attacks embedded in documents or web content
- Rapid adoption of unapproved or “shadow” AI tools before security teams gain visibility
From a security perspective, endpoints are no longer just where attacks land—they are where AI-driven business decisions increasingly originate.
From Blocking Malware to Controlling AI Workflows
This evolution requires a fundamental rethink of endpoint security. In the AI era, cybersecurity is no longer only about preventing malicious execution; it is about controlling AI workflows.
Several priorities emerge:
- Visibility – understanding which AI tools are being used, by whom, and for what purpose
- Input-level data protection – controlling sensitive data at the point it is entered into AI systems
- Policy-driven enablement – allowing approved AI tools, datasets, and use cases instead of attempting blanket bans
- SOC and XDR integration – ensuring AI usage events are monitored, correlated, and investigated like any other security signal
Rather than waiting for a perfect future-state architecture, the emphasis is on actionable steps organizations can take today, starting at the endpoint where AI usage is already occurring.
AI Security Expands Beyond IT into OT and Physical Environments
AI introduces even greater complexity in operational technology (OT) and critical infrastructure environments. In traditional IT environments, AI failures may lead to incorrect analysis or data leakage. In OT environments, AI-driven errors can result in physical damage, production outages, safety incidents, or supply-chain disruption.
As a result, security priorities shift:
- Availability and safety often take precedence over confidentiality
- Legacy systems constrain rapid architectural change
- Security controls must be non-intrusive to avoid operational disruption
Introducing AI into OT therefore requires restraint. Rather than deploying AI directly into control layers, organizations should adopt a gradual approach—starting with monitoring, analytics, and decision support—while strengthening foundational OT security practices such as asset visibility, segmentation, controlled access, and anomaly detection.
What CISOs Should Do Next
For CISOs, the challenge is no longer whether AI will be adopted—it already is. The real question is how to regain control without slowing the business down.
Based on the discussion with Vincent and broader enterprise patterns, several priorities stand out.
First, establish visibility into AI usage at the endpoint.
Security leaders should assume AI tools—both approved and unapproved—are already being used. The immediate goal is to identify which AI services employees interact with, and in which business contexts.
Second, move data protection closer to the point of interaction.
When sensitive data is entered directly into prompts, perimeter-based controls are no longer sufficient. CISOs should focus on protecting data at the moment of AI interaction, not only after exposure has occurred.
Third, define “allowed AI” rather than attempting blanket restrictions.
Banning AI outright is rarely effective and often accelerates shadow adoption. More successful organizations clearly define approved AI tools, approved data types, and approved use cases—providing secure alternatives instead of resistance.
Fourth, integrate AI usage into SOC and XDR workflows.
AI-related anomalies should be treated as first-class security events. Integrating AI usage signals into SOC operations enables monitoring, investigation, and accountability.
Finally, be deliberate with AI in OT and critical environments.
In industrial contexts, CISOs should prioritize resilience over speed. Gradual AI adoption, combined with strong OT security fundamentals, reduces risk while building organizational confidence.
Together, these actions represent a shift from securing systems around AI to governing decisions made with AI.
The Real Question for Security Leaders
The core takeaway from this discussion is not about chasing the latest AI capabilities. It is about starting early, acting pragmatically, and staying in control.
AI security does not need to be treated as a massive future-state program. The most resilient organizations are those that begin with what they can control today, learn quickly within well-governed environments, and expand responsibly as AI adoption grows.
In the AI era, cybersecurity must evolve from protecting infrastructure to governing AI-driven decisions—starting where those decisions increasingly happen: at the endpoint.
AnLab AI PLUS enables fast and secure AI transformation.
From endpoints to CPS environments, discover AhnLab AI PLUS, designed to intelligently enhance security operations across the entire organization.
About AhnLab
Headquartered in South Korea, AhnLab is a growing global cybersecurity vendor supporting enterprise, public-sector, and critical-infrastructure organizations. The company has expanded from its roots in endpoint security to deliver capabilities across endpoint protection, XDR, threat intelligence, and operational technology security, with a strong emphasis on pragmatic, customer-driven security adoption.
