As AI adoption continues to expand across the Middle East, spanning sectors such as government, financial services, energy, and critical infrastructure, organizations face increasing pressure to secure AI applications throughout their lifecycle. From the data used to train models to the deployment of the models themselves, CISOs and IT leaders must manage emerging risks while maintaining digital trust. In response, Cisco has highlighted four priority focus areas organizations should consider to secure AI applications as they scale adoption. The guidance outlines how security teams can adapt established application security practices to AI, helping organizations reduce risk without slowing innovation.
The first focus area is open-source scanning. AI application development often relies on components such as open-source models, public datasets, and third-party libraries. While these resources accelerate development, they may contain vulnerabilities or malicious insertions that could compromise the entire system. Regular scanning of these components helps identify and mitigate risks early in the development process.
The second focus area is vulnerability testing. Static testing involves validating all components of an AI application-including binaries, datasets, and models-to detect potential vulnerabilities, such as backdoors or poisoned data. Dynamic testing evaluates how models perform under various scenarios in production. Cisco also recommends algorithmic red-teaming, which simulates a broad range of adversarial techniques without requiring manual testing, to strengthen model resilience.
