Cybersecurity Trends for 2026 | From Zero Trust to AI Agent Security
September 2026 | SKYWARE INSIGHT
As enterprises expand their use of AI, cybersecurity is changing too. In the past, the priority was keeping external attackers out of corporate systems. Now, companies must also manage which data AI can access and which tasks it is allowed to perform.
Generative AI has moved beyond writing and analyzing documents into software development, customer service, system operations and many other areas of work. AI agents in particular raise new security challenges, because they can connect to external systems, retrieve information and carry out real tasks. At the same time, AI is also being used as a security technology to detect cyberattacks and analyze vulnerabilities.
The key shift in enterprise security in 2026 is that using AI to protect systems and protecting the AI that does the work have become equally important.
Diagram · Three Directions for Enterprise Security in the AI Era
- AI for SecurityStrengthening security with AIThreat detection · Log analysis · Vulnerability checks
- Security for AIProtecting AI itselfData protection · Permission limits · Execution control
- Secure AI IntegrationConnecting safely to enterprise systemsAPI security · Authentication · Access logs
Wider use of AI+Stronger security controls=Trustworthy enterprise AX
01. AI Is Both a Cybersecurity Risk and a New Line of Defense
Advances in AI are affecting both sides of cybersecurity: attack and defense. Attackers can use generative AI to craft sophisticated phishing messages or automate the preparation of attacks. Security teams, on the other hand, can use AI to analyze massive volumes of system logs and find abnormal access patterns and security vulnerabilities more efficiently.
The same shift is visible in Korea. In its plan for the second-half 2026 cyber crisis response drills, announced in September 2026, the Korea Internet & Security Agency (KISA) said it would pilot AI agents for simulated penetration testing of corporate websites. AI will be applied to checks that security experts previously performed by hand, deriving attack scenarios tailored to each website and assessing the likelihood of a breach.
This shows that AI has moved beyond being a simple productivity tool and is now used in the detection, analysis and testing stages of cybersecurity. That said, AI-generated analysis is not always accurate. Whether a threat is real, and how to respond, must still be decided through security policies and verification by the people in charge.
02. The Rise of AI Agents Brings New Security Risks
Where traditional chatbots answered questions, AI agents can connect to enterprise systems and actually get work done. A commerce AI agent, for example, can be designed to search product information, look up order history and, within permitted limits, even perform order-related tasks. The problem is that the more tasks AI can handle, the more information it may be able to access and the broader its execution permissions may become.
There are three main security risks with AI agents.
- ① Data leakageSensitive information can be exposed when AI accesses internal company data such as customer information, contracts or source code, or sends it to external services.
- ② Prompt injectionAn attack in which malicious instructions embedded in external documents or web pages influence the AI’s judgment. If AI mistakes untrusted information for legitimate work instructions, it may perform unintended actions.
- ③ Excessive execution permissionsIf an AI that only needs read access is also given permission to modify, delete or send data externally, the damage from an error or attack can be far greater.
In early 2026, the U.S. National Institute of Standards and Technology (NIST) also published a concept paper on “AI agent identity and authorization,” identifying agent identification, authorization, delegation of access and logging as key challenges. In September, it released guidance (NIST IR 8587) on preventing the forgery, theft and misuse of the tokens and credentials that agents use. What AI can do, not just what it knows, has become the new core of security.
03. Zero Trust Must Now Apply to AI as Well
Zero Trust is a security principle under which users and devices are not trusted by default, and identity and permissions are verified whenever a system is accessed. In environments where AI agents take part in business operations, this principle must apply not only to people but to AI as well.
For example, even if an AI personal shopping can look up a customer’s order history, it must not be able to view other customers’ personal information or download the entire member database. Saving a report written by AI and sending a message to a customer should also be managed under separate permissions. This makes the following design elements essential for enterprise AI systems.
- Least privilege Limit AI to only the data and functions it needs for the task
- Execution approval Require human approval or a separate control procedure for critical actions such as sending, payment and deletion
- Access logging Record the information AI retrieves and the actions it performs so they can be traced
- Data protection Control the external transfer of personal information and credentials, and restrict access to critical data
Above all, security must not rely on instructions given to the AI alone. Permissions must be enforced at the actual API and database level so that AI cannot carry out actions beyond its permitted scope. This is why existing authentication and permission management frameworks become even more important when building enterprise AI.
04. The Strength of AI Security Lies in Safe Integration with Existing Systems
Applying AI to business operations requires integration with existing systems such as ERP, CRM, commerce platforms and messaging systems. But the more systems are connected, the more complex the paths that data travels and the access permissions involved become.
For instance, for a commerce AI agent to support everything from product recommendations to order lookups and customer service, it must draw on data from multiple business systems. This requires deciding what information it can retrieve, how access to each customer’s data will be separated, and which actions require approval. The same applies to development environments. When using AI-powered coding tools, it is necessary to clearly define the scope of source code access, API key management, automatically executed commands and permissions to deploy to production.
An enterprise AI and AX transformation, therefore, is not complete simply by adopting an AI model. Only by building data protection, access permissions and execution controls between AI and existing systems can AI be used reliably in real operating environments.
05. SKYWARE’s View on AI and Security
Drawing on its experience building and operating commerce, messaging and business systems for enterprises, SKYWARE is focused on the potential of connecting AI with existing systems. Commerce platforms and unified messaging systems in particular process customer information and business data and connect with a wide range of external services, making them areas where security and stability must be considered together from the system design stage.
As the use of AI expands into AI-driven customer experience (CX), business automation and AI agents, technologies for precisely managing data access and execution permissions will become increasingly important. Amid these changes, SKYWARE sees not only the potential of AI technology but also stable integration with existing systems, permission management aligned with business processes, and sustainable operating frameworks as key factors.
In the AI Era, Security Is the Foundation for Innovation
Greater use of AI does not mean the answer is to block access to every system. What matters is designing AI so that it can safely use the information it needs and perform tasks within its permitted scope. AI can be a new means of detecting and responding to cyber threats, but it is also a new actor in the workplace that must be protected and managed.
Going forward, a company’s AI competitiveness will depend not only on how much work its AI can do, but on how safely and reliably it does that work.
Security is not a barrier that limits AI innovation; it is an essential foundation for applying AI to real business operations.
Sources: Korea Internet & Security Agency, “Call for Participating Companies in the 2H 2026 Cyber Crisis Response Drills” (September 2026) · NIST NCCoE, “Software and AI Agent Identity and Authorization” concept paper (February 2026) · NIST IR 8587 (September 2026)