Photo By: Zulfugar Karimov
Every October, Cybersecurity Awareness Month brings renewed attention to the threats organizations have spent years learning to recognize: compromised credentials, phishing attempts, malware, exposed data and attackers exploiting software vulnerabilities.
AI agents introduce a less familiar scenario.
What happens when nothing has been hacked, the credentials are legitimate and every individual system is functioning as designed, but an authorized AI agent still takes an action the organization never intended?
As companies give AI systems greater access to databases, APIs, internal applications and business workflows, the distinction matters. The next layer of enterprise risk may not always begin with someone breaking into a system. It could begin with software already authorized to be there behaving in an unexpected way.
That doesn’t make conventional cybersecurity less important. It means increasingly autonomous software is expanding what organizations have to monitor.
When “Authorized” Doesn’t Mean “Safe”
Traditional security controls are designed to answer critical questions.
Who is accessing a system? Are they authorized to be there? What resources can they access? Is someone attempting to exploit a vulnerability?
AI agents add another question: What is the system actually doing with the authority it has been given?
An agent might legitimately have permission to query customer records, interact with an internal application or trigger an external service. But legitimate access does not guarantee that every decision made using that access will align with what the organization intended.
The issue is already recognized within AI security. OWASP identifies “excessive agency” as a risk in LLM-based systems, describing scenarios in which an AI system’s ability to call functions or interact with other systems can enable damaging actions following unexpected, ambiguous or manipulated outputs.
For engineering teams, that creates a gap between determining whether an application is secure and determining whether it behaves appropriately.
Pramin Pradeep, CEO of BotGauge, an Agentic Autonomous QA company, argues that this is where quality assurance is beginning to play a broader role. As software becomes more autonomous, teams increasingly need to validate behavior continuously rather than rely exclusively on predefined checks before deployment.
That does not turn QA into cybersecurity. Instead, it adds another layer of visibility into risks that may emerge even when traditional access controls are working correctly.
The Failure Can Hide Between Systems
The problem becomes harder when AI agents stop operating independently.
Consider an agent that receives information, reasons about what to do, calls an API, passes its output to another system and ultimately updates a database.
The database could be secure. The API could function correctly. Each agent could pass its individual tests. The combined workflow could still produce an unexpected result.
Modern AI applications increasingly depend on chains of components whose behavior affects everything downstream. An unusual API response could influence an agent’s next action. A permissions change could expand what it is capable of doing. Another agent could supply incorrect information that is subsequently treated as reliable.
In those environments, testing every component independently provides only part of the picture.
Engineering teams also need to understand what happens when those components interact under conditions developers may not have anticipated when the original test cases were written.
That is one reason continuous testing is becoming more relevant to AI-native software. BotGauge’s Autonomous QA as a Solution model uses AI agents to generate, execute and maintain tests as applications change, while human domain experts validate results. Rather than treating testing solely as a checkpoint before a release, the approach is designed to keep testing embedded in the development lifecycle.
The underlying principle is increasingly important: autonomous software creates behavioral possibilities faster than humans can manually write tests for every one of them.
Cybersecurity Awareness Needs to Expand Beyond Access
None of this means organizations should replace security teams or conventional cybersecurity tools with QA. The two answer different, sometimes overlapping questions.
Cybersecurity can identify malicious activity, vulnerabilities, compromised credentials and unauthorized access. Behavioral testing can help determine whether software that is authorized to operate is actually producing the outcomes an organization expects.
As AI agents gain more independence, companies may need visibility into both.
This Cybersecurity Awareness Month, that leaves engineering and business leaders with several questions worth considering.
- Do they know which decisions their AI agents are authorized to make, rather than simply which systems they can access?
- Are they testing how agents use that authority after deployment, or assuming successful pre-production testing will remain representative indefinitely?
- And can their monitoring detect a situation in which an AI agent technically completes its task successfully but produces an outcome the organization considers unacceptable?
That last question may become particularly important. A system does not have to crash to fail. An API does not have to return an error. An attacker does not necessarily have to penetrate the network.
An autonomous agent can use authorized tools, receive successful responses and complete the objective it was given while still producing an outcome its creators did not anticipate. Cybersecurity’s established defenses remain essential. Autonomous systems simply add another dimension to what businesses need to defend.
In that environment, knowing whether software is secure is only part of the question. Organizations increasingly need to know whether it is behaving as expected, too.