AI protection today is fragmented and inconsistent. There are two main layers of defense — those implemented by model developers and those applied by deployers or end users.

1. Developer-level protection

   Developers typically address a limited set of well-known technical risks during model training and release. These protections are often implemented at the model architecture or training-pipeline level.

   Standard risks (commonly mitigated by AI model developers):

   • Data poisoning — malicious manipulation or contamination of training datasets.
   • Model extraction — unauthorized replication of the model through excessive querying or weight leakage.
   • Backdoors / trojans — hidden malicious triggers or behaviors implanted during training.
   • Jailbreaks / prompt injection — adversarial inputs designed to bypass safety or alignment constraints.
   • Toxic / prohibited content generation — prevention of hate speech, bias, or disallowed topics.
2. Deployer / operator-level protection

   Organizations that integrate or operate AI models may add their own safeguards, for example:

   • Fine-tuning or reinforcement learning on custom datasets to align behaviour.
   • Occasional penetration testing or red-teaming to probe for vulnerabilities.
   • AI firewalls or filtering layers that monitor and block unsafe inputs and outputs.

   However, these measures are typically reactive, manual, and irregular, lacking real-time correlation and unified monitoring.

We eliminate two major limitations of existing AI security systems:

1. Slow response to emerging threats.
2. Lack of adaptation to company-specific business risks.

Our system operates as a continuous, self-learning red-teaming loop. When a new threat is detected by any of our Clients — whether identified by the AI model itself, by a company analyst, or by an external AI firewall or DLP system — it is sent to our project’s central analysis hub.

New attack vectors are added to the red-team scenario set and are periodically tested across all client models and configurations. Each company maintains its own risk profile, including confidential data, restricted operations, and regulatory requirements. The system uses this profile for personalized testing, verifying whether a new attack vector can impact business-critical areas.

Automated penetration tests — incorporating new threats and client-specific business risks — are configured via the client’s dashboard. They can be executed in both test and production AI environments, but always in safe mode. All vulnerable instances automatically receive countermeasures: updated filters, blocks or firewall rules, and alert notifications.

As a result, protection becomes continuous, self-adjusting, and context-aware, ensuring real-time security without delays or manual intervention.