Protectiv / Applied innovation
Where research becomes capability.
Protectiv combines security research, applied AI, and product engineering to explore recurring security problems, test new methods, and turn validated ideas into working systems.
Verified evidence
Testable capability
Focused systems
Current focus areas
Problems that reward a closer look.
These programs describe where we are building capability. They are not product categories or promises that every experiment will ship.
- 01
AI-assisted security testing
Machine learning systems, including LLMs and agents, that expand test coverage while scope, evidence, and conclusions remain human controlled.
- 02
Attack and abuse behavior
Methods for observing how fraud, message abuse, and adversarial behavior change across channels, systems, and operating conditions.
- 03
Digital investigation systems
Infrastructure that helps investigators connect fragmented public signals, preserve context, and move from query to useful evidence faster.
- 04
Application and API analysis
Techniques for mapping exposed interfaces, testing their behavior, and identifying the conditions that create operational security risk.
Innovation method
Move from an observed condition to something testable.
Each stage narrows uncertainty. Progress depends on the quality of the evidence, not the novelty of the idea.
- 01
Observe
Start with behavior in a real system, threat, workflow, or exposed service.
- 02
Model
Map the actors, states, dependencies, and assumptions that shape the condition.
- 03
Test
Use manual, automated, and AI-assisted methods to explore the boundaries.
- 04
Validate
Reproduce important behavior and separate useful evidence from generated noise.
- 05
Build
Turn a recurring, well-bounded problem into a capability another person can use.
AI-assisted security testing
Models extend the investigation. Evidence decides the finding.
Machine learning, LLMs, and agents can generate test paths, explore state space, correlate signals, and prioritize unusual behavior. They increase reach, but they do not replace technical verification.
Operating rule
Model output is a lead, not a finding.
- AI / 01
Explore
Models and agents help propose test paths, inputs, and relationships worth examining.
- AI / 02
Execute
Controlled tooling runs within an explicit target, permission set, and testing boundary.
- AI / 03
Verify
A researcher reproduces the behavior, inspects the evidence, and determines what can be claimed.
Possible outcomes
Useful work can take more than one form.
Innovation is not a requirement to launch a product. The right result may be evidence, a method, a reusable capability, or a decision not to build.
- 01
Technical finding
A verified condition with enough evidence for another technical team to inspect.
- 02
Published method
A repeatable way to investigate, measure, or reason about a security problem.
- 03
Internal capability
Reusable tooling that improves how Protectiv conducts research or operates systems.
- 04
Working prototype
A bounded experiment that tests whether software can change the outcome.
- 05
Focused product
A dependable system with a clear user, job, and operating boundary.
- 06
Research partnership
A shared investigation or pilot with aligned technical and operational goals.
Collaboration
Bring us a problem worth testing.
We are open to technical partnerships, shared investigations, and carefully scoped pilots with organizations that can bring a real problem and the context needed to understand it.