Artificial intelligence is no longer just changing how applications are developed or tasks are automated. It is also transforming the way security vulnerabilities in software are detected. Gartner places AI-driven vulnerability discovery as the top emerging risk for organizations in Q2 2026, surpassing agentic AI, data integrity issues, or template preparedness.
The essentials of AI and vulnerabilities in 20 seconds
- Gartner is placing AI-based vulnerability discovery at the top of its list of emerging risks for the first time.
- The study gathers responses from 316 executives and risk officers.
- Agentic AI rises to the third position.
- The risk emerges when fault detection advances faster than validation and patching.
- For IT departments, this could accelerate the shift toward more continuous and automated vulnerability management.
The report is particularly relevant for system, security, and development leaders because it signals a scale of change. AI tools can analyze large amounts of code, search for vulnerable patterns, and automate parts of work that traditionally took hours from specialists.
This also benefits defensive teams. The problem arises when the speed of finding errors outpaces organizations’ ability to verify, prioritize, and deploy fixes.
The new bottleneck may be in fixing, not finding
Gartner’s report is based on a survey conducted during April and May 2026 with 316 senior risk officers and executives across various sectors and regions.
The results show a clear technological emphasis:
| Position | Emerging Risk | Previous Position |
|---|---|---|
| 1 | AI-based vulnerability discovery | New entry |
| 2 | Geopolitical energy supply crises | New entry |
| 3 | Agentic AI | 4 |
| 4 | Data integrity | 1 |
| 5 | Lack of preparedness for AI | 5 |
The top risk doesn’t mean any AI model can automatically discover critical vulnerabilities and turn them into attacks instantly.
The process remains multi-phased. It involves locating potentially vulnerable behavior, demonstrating the presence of a problem, determining affected versions, studying exploitability, and developing fixes.
The difference now is that parts of this process can start to be automated at a scale difficult to achieve with manual analysis alone.
Google, for example, has documented using AI systems to identify vulnerabilities in software projects. They’ve worked with initiatives like Big Sleep and OSS-Fuzz and are also developing CodeMender, aimed at leveraging AI agents to help repair vulnerabilities.
This might seem paradoxical: having better tools to find flaws could initially lead to an increase in known vulnerabilities.
From a system administrator’s perspective, that is not necessarily bad news. Finding and fixing an issue before it’s exploited is preferable to a vulnerability hidden in a library used by thousands of applications for years.
The real issue is what happens when the ability to discover vulnerabilities exceeds the ability to fix them.
From patch cycles to more continuous security
For years, many companies have managed vulnerabilities through predictable processes: regular scans, risk classification, testing, maintenance windows, and deploying updates.
AI could challenge this model.
If agents begin continuously reviewing repositories, dependencies, firmware, and applications, the volume of potential findings could grow significantly. Security teams will then need to determine which represent real risks.
This makes prioritization an increasingly critical element.
A critical vulnerability in a library present on hundreds of servers might not be exploitable in a specific configuration. Meanwhile, a seemingly less severe issue in a service exposed directly to the internet might require immediate action.
Severity scores will still be useful but must be combined with data on exposure, configuration, known exploits, and asset importance.
Gartner emphasizes the need to accelerate response strategies, improve remediation, and revisit risk assessment in a landscape where discovery can happen continuously.
For IT departments, this may imply greater integration between asset management, software inventory, vulnerability analysis, development, and patch automation.
It also increases the importance of knowing exactly what software exists within the infrastructure.
Detecting a vulnerability is less useful if an organization takes days to identify which servers use the affected library.
AI also plays a role on the defensive side
The same technology raising concerns among risk officers can be employed to enhance security.
AI agents can assist in reviewing code, analyzing dependencies, identifying risky configurations, generating tests, and suggesting fixes. This opens the door for simultaneous discovery and remediation.
The technical goal might end up being an increasingly automated cycle:
detect → validate → prioritize → fix → test → deploy.
Human intervention remains crucial. For critical infrastructure or enterprise applications, verifying that a patch does not cause additional issues is essential.
Updating a library in a modern web application differs significantly from modifying industrial software, legacy systems, or platforms that must run continuously.
This introduces another well-known challenge for sysadmins: legacy software.
Many organizations still run applications built years ago that rely on outdated libraries, unsupported versions, or components that are difficult to update. Automatically finding more vulnerabilities in such environments doesn’t necessarily make their resolution easier.
When immediate patching isn’t feasible, measures such as network segmentation, service isolation, least privilege, traffic controls, and reducing the exposed surface become even more important.
Agentic AI is also among the major risks
The report highlights another significant shift for the tech sector. Agentic AI moves from fourth to third place among emerging risks.
Here, the concern shifts.
Agents can use tools, access applications, query databases, and perform actions. As their autonomy increases, the importance of permissions and control mechanisms rises.
For system teams, this echoes a long-standing question: what can an identity do within the infrastructure.
An agent with excessive permissions can become a problem, whether due to attack, misconfiguration, or improper instructions.
Traditional security principles remain relevant: least privilege, separation of duties, activity logging, temporary credentials, and monitoring sensitive operations.
The difference now is that identities will no longer only represent individuals, services, and apps. Companies will need to manage agents capable of executing entire operation sequences.
Gartner also ranks data integrity in fourth place and lack of preparedness for AI in fifth—three of the top five emerging risks are directly related to this technology.
For IT departments, the message is less futuristic than it might seem. AI is increasing the speed of existing processes: finding errors, analyzing software, decision-making, and actions.
The infrastructure will need to respond by also increasing the speed of its controls.
Frequently Asked Questions
Can AI automatically find software vulnerabilities?
Systems already exist that can assist in locating vulnerabilities and potentially risky behaviors in code. However, finding a potential flaw doesn’t necessarily mean it can be exploited, and validation remains necessary.
Why does Gartner consider this a risk?
Because the automated capability to discover vulnerabilities might outpace the processes companies use to analyze and fix them.
Can AI also be used to fix vulnerabilities?
Yes. One active area is leveraging agents to analyze code, suggest patches, and verify fixes. Google, among others, is exploring this approach.
What does this mean for system administrators and IT teams?
It increases the need for accurate inventories, understanding software dependencies, prioritizing vulnerabilities based on actual exposure, and reducing the time required to test and deploy updates.
