Get the Most from Testing Your AI-Powered Application

TL;DR Penetration testing delivers value when it is treated as a decision-making tool, not a checkbox. Clear scoping defines why the test exists, what depth is required, and how AI-enabled features change risk. Execution depends on practical readiness, access, data, and fast communication, while meaningful findings come from following issues to real impact. Results only matter if they are framed around patterns, limits, and ownership, so teams can act on what the test reveals rather than file the report away.

After more than a decade of penetration testing, certain patterns repeat themselves.

We’ve found that most disappointing engagements do not fail because the testing was poor. They fall short because the engagement was never aligned on what questions needed to be answered, what mattered most, and how the results would be used. Those problems show up long before testing begins. 

Applications have also grown more complex. Cloud infrastructure, third-party services, SaaS platforms, and AI-driven features are now part of the baseline.

What follows is a grounded look at how application penetration testing works in practice, where it commonly breaks down, and how security leaders can shape engagements that produce lasting value.

Scoping: Defining the Questions

Most of the outcome of a penetration test is decided before testing begins.

Scoping is where intent turns into structure. It is where teams decide why the test exists, what success looks like, and what tradeoffs they are willing to accept. When scoping is rushed or treated as a contractual formality, testing effort gravitates toward what is easiest to reach rather than what carries the most risk.

THE WHY

The first responsibility of a security owner is to be explicit about why the engagement is happening. In some cases, the driver is technical. New functionality is about to ship. Workflows are becoming more complex. Sensitive data is moving through new paths. In others, the driver is business related. Acquisitions, regulatory pressure, or leadership scrutiny often bring testing forward. There are also organizations where security is intrinsic to the product, and penetration testing is part of maintaining confidence as the system evolves.

All of these are valid reasons to test, but problems arise when they are not articulated. A team trying to avoid reputational fallout expects different outcomes than a team trying to understand long-term architectural risk. If those expectations are not aligned upfront, the test will deliver results that feel unsatisfying even when the technical work is sound.

This clarity matters even more as applications incorporate AI-driven functionality. “Test the app” stops being a meaningful objective once parts of the system rely on automated decisions, probabilistic outputs, or external model services. AI introduces risks such as prompt injection, data leakage, and abuse of automated behavior, but those risks are not uniform. They require different depth, different expertise, and different interpretations of impact. Some engagements still aim to identify as many vulnerabilities as possible within a fixed window. Others focus on a narrow set of outcomes or “crown jewels.” Security leaders need to make those priorities explicit before testing begins.

THE TYPE

Scoping also requires clarity around what kind of test is being run. Application penetration testing can mean very different things depending on context. Some engagements prioritize breadth and speed, supported by internal access, source code, and documentation. Others simulate what an external attacker could accomplish with limited information. In some cases, the application itself is not the primary subject, and the real question is whether defensive controls behave as expected under pressure.

None of these approaches are wrong, but they answer different questions. Confusion arises when one model is assumed by the client and another by the testing team.

THE FOCUS

Modern systems also demand more context than they used to. Too little information is far more damaging than too much. Applications today depend on APIs, third-party services, SaaS platforms, and managed components that influence core behavior. When pieces of that picture are missing, testers either make assumptions or avoid entire areas of the system. When uncertainty exists, narrowing focus is usually safer than shrinking scope. Including the full system allows testers to understand how components interact and where trust boundaries actually exist, even if not every component receives the same depth of scrutiny.

This becomes especially relevant with AI-enabled features. Teams often rely on models, pipelines, or services they did not build and do not fully control. From a testing perspective, this is not a fundamentally new problem. It is another opaque dependency. What matters is where that dependency sits, what it influences, and what happens when it behaves in unexpected ways. Documenting model interactions and data flows still matters, even when exploitation depth is limited. Visibility creates context, and context reduces blind spots.

Scope and focus play different roles here. Scope defines what is legally allowed to be tested and should be fixed. Focus determines where effort is spent and should remain flexible.

THE DEPTH

Depth is the final scoping decision that blends technical reality with business constraints. Every penetration test runs within a fixed window. Some engagements are intentionally shallow, designed to surface severe failures quickly. Others are meant to explore workflows and systemic weaknesses in detail. Both approaches work when expectations match the investment, but friction often appears when teams expect exhaustive insight without allocating the time required to achieve it.

A related decision that often goes unexamined is whether the engagement is meant to test the application or the controls surrounding it. Most organizations want insight into application behavior. In those cases, controls such as WAFs are better disabled, so they do not absorb time and obscure underlying issues. There are engagements where testing controls is the explicit goal, and that is reasonable. What causes trouble is leaving controls enabled by default and treating blocked traffic as success. Automated tools will collide with controls quickly, and that can hide weaknesses in authorization, business logic, and AI-specific handling that only appear when the application itself is exercised.

Fieldwork: Where Execution Either Builds Insight or Burns Time

Once testing begins, most engagements look healthy on the surface.

Access is provisioned. Timelines exist. Everyone expects a report at the end. The problems that undermine fieldwork tend to be mundane and operational, not technical. They surface after time has already been lost and momentum has faded. Here are the most common:

Access issues are among the most common. Credentials fail. Applications are only reachable from internal networks. VPN access was assumed but never validated. In some cases, testers log in on the first day only to find that core functionality is broken. These problems are rarely intentional. They are symptoms of rushed preparation.

Data quality is another frequent blocker. Testing access control requires multiple users and a realistic state. Environments with a single account or empty datasets prevent entire categories of issues from being evaluated. No amount of tester skill can compensate for an environment that cannot express meaningful behavior.

Communication during fieldwork plays a larger role than many teams expect. Questions inevitably arise about expected behavior, role definitions, and edge cases. When answers are slow, testers either wait or proceed with assumptions. Neither produces strong results. Effective engagements designate a technical contact who can respond quickly and directly.

Status meetings serve a different purpose. They are best used to confirm progress, surface blockers, and validate that the engagement is still aligned with its goals. They are not the place for daily findings. Early reporting carries the risk of false positives or incomplete understanding, and it can prematurely lock conclusions that testing has not yet validated.

One of the most important responsibilities during fieldwork is allowing testers to follow issues to their real impact. A vulnerability in isolation rarely tells the whole story. The value of penetration testing comes from understanding what a weakness enables and how far it can be taken. That often requires chaining issues together and observing how systems interact under stress.

This dynamic becomes more pronounced when applications rely on AI-driven behavior. When outputs are probabilistic or mediated by external services, the first observable issue is often less important than what happens next. If an output influences access, workflow progression, data handling, or automation, the risk frequently emerges several steps downstream. Fixing issues the moment they are discovered can cut off that exploration and leave teams with an incomplete understanding of impact.

Delivery: Turning Technical Findings into Organizational Decisions

Delivery is where many otherwise strong engagements quietly fail.

The report is delivered. The work is praised. Then time passes. Priorities shift. People change roles. But the findings never translate into lasting improvement.

The report readout is the opportunity to prevent that outcome. It is where the assessment team explains not just what was found, but how the engagement unfolded. Patterns, recurring weaknesses, and systemic issues matter more than individual findings. This context helps security leaders separate signal from noise.

Understanding limits is just as important as understanding findings. No engagement covers everything. This is especially true when parts of the system depend on third-party or managed components. For those areas, including AI-enabled features, clarity around assumptions and coverage boundaries matters. Security owners should understand what was exercised, what was inferred, and what was out of scope in practice, not just on paper.

Counting vulnerabilities rarely tells a useful story. A long list does not automatically indicate a weak system. A short list does not guarantee safety. What matters is how issues relate to one another and what they reveal about deeper problems. Repeated authorization failures, unclear trust boundaries, and fragile automated workflows point to systemic risk that individual fixes may not resolve on their own.

A penetration test only creates value when findings lead to action. That requires planning before the report arrives. Remediation ownership should be clear. Expectations should be set around which teams will be involved, particularly when findings span security, engineering, data, and legal concerns. Decisions around mitigation and risk acceptance, especially for model-related issues, should be deliberate rather than reactive. When ownership is unclear, reports tend to sit idle, and organizations sometimes return months later to find the same problems still present.

Closing Perspective

An application penetration test is not defined by the number of vulnerabilities it uncovers.

Its value lies in clarity. Clarity about where risk exists, why it exists, and what needs to change to reduce it. That clarity is built across the full lifecycle of the engagement. It starts with disciplined scoping, depends on thoughtful execution, and only pays off when results are understood and acted upon.

When any part of that chain breaks, the test feels disappointing even when the technical work itself was strong. For security leaders and AppSec owners, shaping that chain is the real work.

For more detailed guidance, we recommend watching Dan Petro’s “Getting the Most of Penetration Tests” virtual session or downloading our guide on running penetration tests for AI-driven applications.