Cisco highlights four priority focus areas that organizations should consider to secure their AI applications as they expand their deployments.
This guidance outlines how security teams can adapt proven application security practices to AI to help organizations across the Middle East manage new risks and maintain digital trust.
As AI adoption expands across the Middle East, including in government, financial services, energy, and critical infrastructure, CISOs and IT leaders are under pressure to protect AI applications throughout their lifecycle, from the data they rely on to the models they deploy.
Four areas of focus for AI application security:
- Open source scanning: AI application development relies heavily on components such as open source models, public datasets, and third-party libraries. These dependencies can contain vulnerabilities or malicious injections that compromise the entire system.
- Vulnerability testing: Static testing of AI applications involves validating AI application components such as binaries, datasets, and models to identify vulnerabilities such as backdoors or contaminated data. Dynamic testing evaluates how your model responds across different scenarios in production. Algorithmic red teaming can simulate a diverse and extensive set of adversarial techniques without the need for manual testing.
- Application firewall: The advent of generative AI applications has given rise to a new class of AI firewalls designed with LLM-specific safety and security risks in mind. These solutions act as model-agnostic guardrails that inspect AI application traffic in transit to identify and prevent failures and enforce policies to mitigate threats such as PII leaks, prompt injections, and denial of service (DoS) attacks.
- Preventing data loss: The rapid adoption of AI and the dynamic nature of natural language content has made traditional DLP ineffective. Instead, DLP for AI applications inspects inputs and outputs to address sensitive data leaks. Input DLP can restrict file uploads, block copy-and-paste functionality, and restrict access to unauthorized AI tools. Output DLP uses guardrail filters to ensure that model responses do not contain personally identifiable information (PII), intellectual property, or other sensitive data.
“As AI adoption accelerates across the region, organizations are rapidly moving from pilot to production, and that transition changes their risk profile,” said Fadi Younes, Managing Director, Cybersecurity, Cisco Middle East, Africa, Turkiye, Romania, CIS. The entire lifecycle must be protected. By applying familiar security principles in an AI-specific way, organizations in the Middle East can confidently scale innovation while mitigating risks such as rapid injections and sensitive data leaks. ”
Protect your AI applications from development to production
Risk exists at nearly every point in the AI lifecycle, from sourcing supply chain components to development and deployment. The security measures highlighted above can help mitigate different areas of risk, and each plays an important role in a comprehensive AI security strategy.
Image credit: Cisco
