Open any security vendor’s website in 2025 and you’ll find AI mentioned within the first scroll. “AI-powered threat detection.” “AI-driven vulnerability management.” “Autonomous security operations.”
The problem? These terms mean wildly different things depending on who’s using them. Some vendors have genuinely rebuilt their products around AI capabilities. Others have added a chatbot to their UI and updated their marketing.
For security leaders evaluating tools, this creates a real problem: how do you separate signal from noise? How do you know if a product’s AI claims represent meaningful capability or just good copywriting?
The Four Patterns of “AI” in Security
After analyzing dozens of products claiming AI capabilities, a clear taxonomy emerges. Most fall into one of four patterns, each with different implications for what you’re actually buying.
Pattern 1: AI-Powered Prioritization
What it is: Machine learning models trained on vulnerability data to rank and prioritize findings.
What it does: Takes scan results and predicts which vulnerabilities are most likely to be exploited, most critical to your environment, or most likely to be false positives.
What it doesn’t do: Actually test anything. The underlying detection is still signature-based or rule-based. The AI layer sits on top, helping you triage the output.
Where you see it: Most major vulnerability management platforms have added this. It’s genuinely useful for reducing noise, but it’s an enhancement to existing scanning, not a new capability.
Questions to ask: “What does the AI actually do? Does it affect detection, or just prioritization of results?”
Pattern 2: Intelligent Automation
What it is: Pre-built workflows with decision trees that adapt based on conditions. Often marketed as “autonomous” or “self-driving.”
What it does: Executes sequences of actions based on if-then logic. If port 443 is open, run SSL tests. If SSL test finds weak cipher, check for related CVEs. The paths are predetermined, but the system chooses which path based on what it finds.
What it doesn’t do: Reason about novel situations. Handle scenarios not covered by its decision trees. Adapt strategy mid-operation based on unexpected findings.
Where you see it: Many “autonomous pentesting” and “automated red team” products. They’re sophisticated automation, and the attack graphs can be extensive, but they’re fundamentally following programmed logic.
Questions to ask: “Can it handle scenarios not in its playbook? How does it decide what to try when standard approaches fail?”
Pattern 3: AI-Assisted Security
What it is: Large language models integrated into security workflows to help humans work faster.
What it does: Explains findings in plain language. Suggests remediation steps. Generates reports. Answers questions about your security posture. Helps write detection rules or security policies.
What it doesn’t do: Take autonomous action. Make decisions without human approval. Execute attacks or defenses on its own.
Where you see it: Security copilots, chatbots integrated into SIEM/SOAR platforms, report generation features.
Questions to ask: “Does the AI execute anything, or does it just advise? Who makes the final decision?”
Pattern 4: Agentic AI
What it is: AI systems that autonomously plan, execute, and adapt operations toward a goal.
What it does: Analyzes targets and decides strategy without human instruction. Takes real actions in the environment. Chains multiple steps together, maintaining context. Adapts approach based on what it discovers mid-operation.
What it doesn’t do: Follow predetermined scripts. Require human approval for each step. Stop when encountering unexpected situations.
Where you see it: True agentic AI in security is still rare. It requires sophisticated architecture: LLM-based reasoning, tool orchestration, environment interaction, and governance controls.
Questions to ask: “Does it plan its own approach or follow scripts? Can it adapt strategy mid-operation? How does it handle unexpected findings?”
We’ve written extensively about what agentic AI means for offensive security and why the distinction matters.
A Note on Hybrid Products
In practice, products often mix patterns. A vulnerability management platform might use Pattern 1 for triage, Pattern 3 for report generation, and claim Pattern 2 for “automated validation.” That’s fine, these capabilities can complement each other.
The question isn’t “which single pattern does this product use?” It’s “which pattern drives the core capability I’m paying for?” If the primary value proposition is autonomous attack discovery, but the actual AI is only doing prioritization and reporting, that’s a mismatch worth catching.
Same Target, Different Patterns: A Concrete Example
To make the distinction tangible, consider how Pattern 2 and Pattern 4 systems handle the same unexpected finding.
The scenario: During a web application test, the system discovers an unusual endpoint: /api/internal/debug. It returns a 403 Forbidden, but the response includes a header X-Debug-Token: disabled.
How a Pattern 2 system responds:
The attack graph doesn’t have a node for “unusual debug endpoints with custom headers.” The system logs the 403, notes it as “access denied,” and moves to the next target in its predetermined sequence. The finding appears in the report as a low-priority “information disclosure” note, if at all.
How a Pattern 4 system responds:
The AI notices the anomaly: a debug endpoint that’s returning a hint about its state. It reasons: “This header suggests the endpoint exists and has a toggle. What if I can enable it?” It tries variations: X-Debug-Token: enabled as a request header, ?debug=true as a parameter, looking for related endpoints like /api/internal/debug/enable.
If one works, it follows the new attack surface. If none work, it notes the finding with context: “Debug endpoint exists, currently disabled, may be enableable through configuration or other vectors. Recommend investigation.”
The difference: Pattern 2 followed its map. Pattern 4 noticed something interesting and investigated. One executes; the other reasons.
This is why demos matter more than datasheets. Ask vendors to show you what happens when the system encounters something it wasn’t specifically programmed to handle.
Why the Distinction Matters
These patterns aren’t just academic categories. They have real implications for what you can expect from a product.
Pattern 1 (Prioritization) helps you work through existing scan results faster. It doesn’t find new vulnerabilities or validate exploitability. If your problem is “too many findings, not enough context,” this helps.
Pattern 2 (Intelligent Automation) scales predefined testing. It runs through attack paths faster than manual testing. But it only finds what it’s programmed to look for. If your problem is “need to run known checks more frequently,” this helps.
Pattern 3 (AI Assistance) makes your existing team more productive. It doesn’t replace human judgment or execution. If your problem is “team spends too much time on documentation and explanation,” this helps.
Pattern 4 (Agentic AI) introduces capabilities that weren’t previously possible at scale: adaptive attack planning, novel vulnerability discovery, attack chain identification that wasn’t pre-programmed. If your problem is “need expert-level offensive testing at continuous scale,” this is the category that addresses it.
The “Autonomous” Trap
The word “autonomous” deserves special scrutiny. It’s become the most abused term in security marketing.
Some vendors use “autonomous” to mean “runs without clicking start.” Others mean “makes some decisions automatically.” A few mean genuine autonomy: the system reasons about goals and decides its own approach.
The test is simple: what happens when the system encounters something unexpected?
- If it stops and waits for human input, it’s automated, not autonomous.
- If it falls back to a default behavior regardless of context, it’s following rules, not reasoning.
- If it considers the new information and adjusts its strategy accordingly, that’s genuine autonomy.
This is why we emphasize mission-driven security testing as a paradigm. Autonomous systems need goals, not scripts.
The Curveball Test
If you want a concrete, repeatable way to evaluate agentic claims, use what we call the Curveball Test.
Setup: Give the system a target it hasn’t seen before-ideally something you control where you’ve planted a non-obvious vulnerability. Not a textbook SQL injection or a known CVE. Something that requires connecting dots: a misconfigured permission that only matters in combination with another endpoint, or an IDOR that requires understanding the application’s user model.
Observe:
- Does the system find it, or only flag standard issues?
- Can you see its reasoning? Why did it try what it tried?
- When it hit dead ends, did it try alternatives or just move on?
- Did it connect findings from different parts of the test?
The bar: A genuine Pattern 4 system should be able to explain its reasoning in natural language, show you the decision points where it chose one path over another, and demonstrate that those decisions weren’t hardcoded responses to specific signatures.
If the vendor can’t show you this-if the AI’s decision-making is a black box, or if every “decision” maps perfectly to a predetermined rule-you’re likely looking at Pattern 2 with good marketing.
This test isn’t about whether the system finds every vulnerability. It’s about whether you can observe it thinking versus executing.
Red Flags in AI Security Marketing
After reviewing hundreds of AI security claims, certain patterns predict hype over substance:
“AI-powered” without specifics. If they can’t explain what the AI does technically, it’s probably Pattern 1 or 3 bolted onto existing technology.
Focus on speed without explaining capability. “10x faster scanning” might just mean parallel processing. Speed claims without capability claims are often automation relabeling.
Demo videos that skip the decision-making. Watch for cuts between “AI analyzing target” and “vulnerabilities found.” The interesting part is what happened in between.
No discussion of governance or safety. Genuine agentic AI in offensive security requires explicit controls. If a vendor claims autonomous attack capabilities but doesn’t discuss how they prevent out-of-scope testing, be skeptical.
Comparisons to chatbots. “Like ChatGPT for security” usually means Pattern 3. Chatbots advise. They don’t execute.
Questions to Ask Vendors
When evaluating AI security products, these questions separate real capability from marketing:
On architecture:
- Is the AI doing detection/execution, or just prioritization/reporting?
- What happens when the system encounters a scenario not in its training data?
- Can you show me the AI making a decision that wasn’t pre-programmed?
On autonomy:
- Does the system plan its own approach, or follow predetermined workflows?
- How does it adapt when initial approaches fail?
- What decisions does it make without human approval?
On governance:
- How do you prevent the AI from operating outside defined scope?
- What audit trail exists for AI decisions?
- How do I verify what the AI actually did during an assessment?
On results:
- Can the system find vulnerabilities not in its signature database?
- Does it chain findings together into attack paths?
- How do you validate that findings are exploitable, not theoretical?
On observability:
- Can you show me the AI’s reasoning trace for a specific finding?
- Can I see where it changed strategy mid-test based on what it discovered?
- When it encounters something unexpected, can I watch how it decides what to do next?
The answers will quickly reveal which pattern you’re dealing with.
Where This Is Heading
The security industry is in a transition period. Genuine AI capabilities are emerging, but they’re buried under a mountain of marketing claims from products that haven’t fundamentally changed.
Over the next few years, expect consolidation. Products with Pattern 1-3 capabilities will need to clearly communicate what they are (valuable enhancements) rather than what they aren’t (autonomous AI). Products with genuine Pattern 4 capabilities will demonstrate it through results that weren’t previously possible.
The market will eventually sort itself out. Buyers will learn to ask better questions. Vendors will face pressure to prove claims. But in the meantime, the burden falls on security leaders to cut through the noise.
The Practical Takeaway
Don’t dismiss AI in security - real advances are happening. But don’t accept AI claims uncritically either.
When a vendor says “AI-powered,” ask which pattern they fall into:
- Prioritization: AI helps you triage. Detection unchanged.
- Intelligent Automation: Pre-programmed logic, flexible execution.
- AI Assistance: LLM helps humans work. Humans still execute.
- Agentic AI: Autonomous planning, execution, and adaptation.
Each has value. None is wrong. But conflating them leads to mismatched expectations and wasted budget.
The question isn’t “does this product use AI?” It’s “what does the AI actually do, and does that match my problem?”
Answer that honestly, and you’ll make better decisions than most of the market.
Understanding AI capabilities is the first step. The next is understanding why continuous security validation matters and how to implement it.