One of the most important questions cybersecurity teams have to deal with is this: How do we identify our vulnerabilities before someone else does? The stakes have never been higher, and the consequences of a missed weakness can be serious. We’re talking about data breaches, financial losses, and reputational damage—to start.

To stay ahead, organizations rely on penetration testing—both automated and manual—to uncover security issues from an attacker’s perspective. However, the decision of whether to automate is as old as the industry itself. Automated tools can scan thousands of hosts in a few hours, but manual testing dives deeper to find more subtle vulnerabilities. Even when testers choose to automate, how far is too far?

We’re here to settle the debate—not by picking a winner but by showing you how to combine these two forces to create a dynamic, unbeatable team.

Automated penetration testing: Speed at scale

Automated scanners are designed to simulate repeatable aspects of an attacker’s workflow, allowing them to scan thousands of systems quickly and autonomously. This means they can identify known vulnerabilities with impressive speed and consistency. Most automated scanners will do the following:

  • Run port and service-based scans to map the attack surface
  • Detect known vulnerabilities via signatures or heuristics
  • Attempt basic proof-of-concept exploitation (e.g., CVEs and SQL injection).

These tools excel at detecting common security flaws like unpatched software, default credentials, and misconfigurations. This is important considering that the 2025 Verizon Data Breach Investigation Report found that the exploitation of known vulnerabilities was the second most common cause of breaches, making up 20% of the total. Popular scanning tools include the following:

  • Nessus—Well-established in enterprise environments for comprehensive network scanning
  • OpenVAS—A capable, open-source alternative focused on network vulnerabilities
  • ZAP—Great for web application testing, especially in DevSecOps workflows
  • nuclei—An extremely customizable vulnerability scanner that detects known CVEs and performs limited web testing.
And it’s that easy to start running scans.

The simplicity and reproducibility of automation lends itself especially well to developers’ continuous integration and continuous delivery (CI/CD) pipelines. When a developer pushes new code or a project is close to a new release, automated scanning can detect misconfigurations, anti-patterns, and outdated dependencies that might have slipped through the cracks of peer review.

Unfortunately, automated scanners are not without their faults. Here are some common challenges:

  • False positives—A scanner may flag a risk that’s not actually exploitable. OWASP research indicates that automated tools have false positive rates between 15% and 30% for common vulnerability types.
  • Poor signal-to-noise ratio—Even if scanners identify a valid risk, they usually have static severity ratings. For instance, a scanner might mark both an end-of-life operating system and a command injection as critical severity. The command injection would clearly have greater impact, but scanners don’t do a great job at capturing nuance.
  • Lack of context—Automation doesn’t understand how your application works or what data is sensitive. Scanners are great at finding what they’re explicitly told to look for, but it’s much harder to codify what is or isn’t an authorization or authentication flaw. You could have your scanner use regular expressions to detect credit card numbers or emails in responses, but there’s a good chance you’ll introduce more false positives that way.
  • Missed complex issues—Business logic flaws and chained vulnerabilities often slip through. Overly permissive CORS policies and security attributes on cookies are frequently overlooked as low-risk issues. That said, with the right circumstances, these misconfigurations can be chained together to enable impactful CSRF or XSS attacks, which can’t be as easily automated without compromising on speed.

Manual penetration testing: Thinking in graphs

You’ve probably heard the phrase “Attackers think in graphs,” which is exactly what manual testing is all about. Manual testing is carried out by skilled security professionals who think like attackers. They combine technical expertise with creative problem-solving to find the flaws that have the greatest impact. Unlike automated tools that follow predetermined scripts, human testers adapt their approaches based on discoveries, often uncovering complex attack paths that tools miss entirely.

A skilled tester excels at the following:

  • Uncovering flaws that require deep contextual understanding and critical thinking.
    Business logic flaws can be extremely impactful but require human understanding of an application’s or a system’s threat model. For example, manual testers understand how an e-commerce business works, so they can easily identify purchase limit bypasses or ways to manipulate price mechanisms.
  • Chaining vulnerabilities. Some of the coolest reports involve taking several minor vulnerabilities and combining them into a vulnerability with more impact. A self-XSS alone might not be very impactful, but skilled testers know that cache poisoning, request smuggling, client-side path traversal, etc. can all make self-XSS much more potent.
  • Verifying findings. Unlike automated scanners, manual testers are much better at evaluating whether a finding is valid by taking context into account.

The reason automated testing and scanning haven’t made manual tests obsolete is because only humans can bring situational awareness to the table. Good manual testing will look at a system’s architecture, individual components, and data flows to understand the big picture and subsequently identify what areas need deep dives. A 2022 study comparing static, dynamic, and manual testing in a Java web app found that exploratory manual pen testing (EMPT) uncovered more severe vulnerabilities and was just as efficient in terms of vulnerabilities per hour as automated techniques.

That said, manual testing has its own flaws:

  • Testing takes time. Manual testing has the potential to go deeper than any automation ever could; however, results do not come nearly as quickly. In a 2024 survey conducted by Cycognito, organizations said that the biggest inhibitor to effective application testing was the sheer number of apps and APIs that need to be tested. As the scope of an assessment scales up, so do the costs.
  • Real people can only provide limited coverage. In practice, most manual testing is done “best effort,” meaning testers try to cover as much as possible, prioritizing the areas of most significant risk. However, it’s not guaranteed that every possible input or data flow was tested.
  • Human error is a fact of life. While automated scanners tend to produce many false positives, manual testing can produce false negatives, such as claiming something is not vulnerable when it is vulnerable.

How they work together

So, is it better to use automated or manual methods? The answer is: use both!

The most effective penetration testing happens when testers use automated and manual testing methods in tandem. Use automated methods to take care of the easy, low-hanging fruit, and use manual testing to take care of the blind spots and dive deeper. Let’s consider an example:

  • A web application needs to be assessed, so a team starts by running Burp Suite’s Active Scan against the application. Meanwhile, manual testers explore the web application to understand the core flows and business requirements.
  • When the Active Scan results are returned, manual testers go through the alerts and verify which findings are valid and which are false positives. They could conduct additional scans, such as having nuclei or Nettacker run in the background to scan for common CVEs and network infrastructure.
  • While triaging alerts, the testers learn that the scanner found a hidden endpoint called /internal/user-info. After some fuzzing, the testers find the id parameter, which allows them to retrieve the email and order history for any user on the platform, a significant authorization vulnerability!

Although it’s possible that someone could have found this vulnerability manually with enough time, the effective use of automation allowed the tester to focus on the interesting stuff and filter out the secure parts of the application early on. This workflow combines the speed of automation with the depth of manual review, delivering more value in a shorter amount of time.

The AI question—does this matter anymore?

The rise of AI has introduced a unique twist on this discussion, compared to a few years ago. The rapid advancements in AI have caused people to rethink the value of manual testing, since generative AI seemingly performs threat modeling and appears more nuanced than straight-up automated scanning. Companies like XBOW and Hacktron claim to almost autonomously perform full security reviews and penetration tests, and projects like CAI have demonstrated the ability to automate defensive tasks as well. Regardless of your position on AI usage, it’s important to be cognizant of the following facts:

  • “Hallucinations” have not been solved. Since generative AI models are still, at the time of writing, predictive, you cannot guarantee that they will always make sense or be truthful. Improvements to models and techniques aim to reduce the likelihood of hallucinations but do not eliminate this risk, which makes it challenging to trust AI output blindly.
  • Human-in-the-loop approaches remain vital. The AI projects that have been making headlines are not fully autonomous. Many of these projects rely on manual human intervention (“human-in-the-loop”) to verify and validate AI output, which sets them up to be a more powerful form of automation than a replacement for manual tests.
  • AI is expensive. Running the strongest models locally requires a lot of physical hardware, which can get very costly very quickly. Although buying a subscription to OpenAI or Anthropic might be cheaper, there’s a chance that prices will go up with future advancements.

This isn’t to say that AI will succeed or fail. Rather, it’ll be interesting to keep an eye on its development. If these problems can be solved and optimized, AI could be an incredible assistant for penetration testing.

What have we learned?

Penetration testing is part of building a real, working security posture. Automation gives you coverage and consistency. Manual testing gives you depth and context. Relying too much on either one means you’re leaving gaps for attackers to wedge themselves into. When you step back and look at the big picture, you realize that the question isn’t about automated vs. manual. Rather, it’s about when and how to use each effectively. Organizations and individuals who find the right balance will consistently outpace those who lean too heavily in either direction.

To ensure the most comprehensive coverage, automate the repetitive stuff, whether it’s port scanning, subdomain enumeration, fuzzing, etc., but also take notice of your attacker’s intuition and situational awareness; manually review any systems that seem more vulnerable. Even as automation techniques get better, and even if AI starts to blur the lines, this combination of methods will cut down on the chances that significant material risk slips through the cracks.

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