Evaluation and testing are two distinct yet overlapping concepts whose lines are often blurred. When it comes to AI systems, these distinctions become really important.

Testing an AI system involves verifying specific behaviors and catching deviations in its operation, you're checking whether it's working as designed. Evaluation involves measuring its capabilities, performance, and alignment across tasks, assessing how well the system works overall.

In the sense of traditional software, this distinction is relatively clear. You test that functions return expected outputs, then evaluate overall system performance. But AI systems introduce complexity: behaviors emerge from training rather than explicit programming, making the boundary between "working as designed" and "working well" much harder to define.

Bringing This to Security

Testing involves checking the system for known vulnerabilities — verifying that documented attack patterns don't work, that previous jailbreaks stay patched, that the model refuses harmful requests it should refuse. Evaluation, on the other hand particularly red teaming, involves probing for unknown vulnerabilities — adversarial exploration asking "how can this be broken?"

Testing is defensive: did we fix this known issue? Evaluation is offensive: what haven't we thought of yet?

The Current State: Evaluation Over Testing

Here's where many companies and organizations building AI systems especially AI agents run into problems. They focus heavily on evaluations while neglecting systematic testing before deployment.

Organizations measure their models on capability benchmarks, run user satisfaction surveys, and conduct performance evaluations. They assess how well their AI performs on reasoning tasks, how accurately it completes objectives, how engaging it is in conversation. This isn't bad in itself, understanding your system's capabilities matters.

But when it comes to security testing? Many skip it entirely or treat it as an afterthought.

The result is predictable: systems fall to known vulnerabilities when encountered by malicious actors. Prompt injection techniques documented months ago still work. Models leak sensitive data through well-published extraction methods. Agents can be manipulated to bypass safety controls using straightforward jailbreaks that anyone could find with basic research.

The Cost of Skipping Tests

It's one thing to test these systems, identify their weaknesses, fix them, and then have malicious actors discover novel attack vectors you hadn't considered. That's the reality of security — there will always be unknown unknowns.

It's entirely different to deploy without running tests at all, allowing your system to fail against known vulnerabilities that could have been caught with basic security testing.

The consequences extend beyond immediate financial losses. Companies face regulatory scrutiny, reputational damage, and erosion of user trust. More critically, vulnerable AI systems deployed in sensitive domains such as healthcare, finance, infrastructure can cause real harm to real people.

Current Testing Practices

Some organizations go the extra mile to test by running their own assessments in-house, which is better than nothing. They might have engineers manually try a few jailbreaks, run some internal red teaming sessions, or check for obvious failure modes.

But in-house testing alone has limitations:

  • Teams develop blind spots about their own systems
  • Internal testers know the guardrails and unconsciously work around them
  • There's often no structured methodology, just ad-hoc probing
  • Known vulnerability databases aren't systematically checked
  • Testing happens once before launch rather than continuously

What Should Change

The path forward requires layering both testing and evaluation:

Start with systematic security testing:

  • Build automated test suites for known attack patterns
  • Check against documented jailbreaks and prompt injection techniques
  • Verify system prompt protection and goal alignment
  • Test for data leakage and training data extraction
  • Run these tests on every model update, like regression tests in traditional software

Then layer on security evaluation:

  • Conduct regular red teaming with both internal and external adversaries
  • Probe for novel vulnerabilities you haven't anticipated
  • Measure robustness across entire threat models
  • Document discovered attacks and convert them to tests

Make it continuous:

  • Security isn't a one-time gate before deployment
  • New attack techniques emerge constantly
  • Your system changes with each update
  • Testing and evaluation should be ongoing processes

The Bottom Line

Testing verifies you've addressed known problems. Evaluation discovers unknown ones. Both are essential, but right now, the industry skews too heavily toward capability evaluation while neglecting security testing.

You can't prevent every possible attack. But you can and should prevent attacks using techniques that are already documented and understood. That's what testing does.

Before asking "how capable is our AI?", ask "have we verified it resists known attacks?" The former is exciting and drives product metrics. The latter prevents your system from being trivially compromised on day one.

Start testing. Then evaluate. Then test what you found. It's not glamorous, but it's how you build AI systems that don't fail the moment they encounter adversarial pressure.