Artificial Intelligence is changing the game for businesses across operations, design, and customer service. AI can do a lot, whether automating complex processes or enhancing user experiences. However, as these systems are adopted across various organizations, a critical question arises: how can we trust them? AI’s strength comes from the same complexity that creates new vulnerabilities that conventional security cannot mitigate.

The stakes are high. Just one security breach in an AI could mean more than just data loss.  It can produce fake outputs, lead to biased decisions, and erode user trust in AI-based systems, products, and services.  An AI under attack, for example, could leak sensitive company information, spread misinformation that causes harm, or act against its own programming. We need a focused and holistic approach to AI security to build this confidence. This means assessing the risk across the application life cycle and updating our defence as necessary. It means moving beyond current cybersecurity solutions and practices to address the specific dangers posed by AI.

The Shifting Threat Model in AI

Keeping AI applications safe isn’t simply about applying old rules to new tech. The threats are fundamentally different. Many systems, such as SAST and DAST, were created for code-based solutions. Solutions of today are based on the cloud. They are good at finding bugs in software. However, they cannot identify threats in AI models, their training data, or the text prompts they use.

Among the most dangerous new attacks is fast injection. An attacker could trick the AI into bypassing its safeguards or leaking data by providing a sophisticated prompt. A group of researchers from places like Carnegie Mellon University learned how you could stick simple “bad action suffixes” on the prompts you sent to large language models (LLMs) to get them to keep creating bad content. This shows that AI guardrails can be easily bypassed when they are not properly secured.

Another major concern is the AI supply chain. Present-day AI applications are often built using pre-trained models, open-source libraries, and large datasets from diverse sources. You have a risk of failure at each component. An attacker can poison a model by injecting harmful data during training. It causes the model to respond unpredictably or enables backdoor access. A recent report found a significant increase in the number of malware AI/ML models uploaded to public repositories such as Hugging Face.

This evidence suggests that cyber attackers are really beginning to see this new vector. Without continuous scanning and verification, organizations could unknowingly integrate compromised components into their systems, a risk that solutions from platforms like Noma Security are designed to mitigate.

Lifecycle Approach to AI Security

Security cannot be an afterthought if we are to create AI that can genuinely be trusted. It has to be incorporated into each phase of the AI application life cycle: during development and training, and at deployment and runtime. This integrated approach means that threats can be discovered and patched before they become attack vectors.

Security During Development and Training: A secure AI application is tuned at its creation. This phase entails a thorough scrutiny of all parts of the AI supply chain.

Data Integrity: The data fed into an AI model should be high-quality, unbiased, and not leaked. Processes to scan for tampered or malicious data that could poison the model should also be in place.

Model Scanning: A pre-trained model must be thoroughly scanned for hidden vulnerabilities or malicious code before being integrated. This is important for models downloaded from public repositories.

Component Analysis: All third-party open-source libraries and frameworks used in AI app development must be verified for known security vulnerabilities. One insecure dependency could provide an attack vector.

By addressing these aspects, development groups can take active steps to mitigate the risk that such insecurities will be constructed into their AI systems from the outset.

Testing and Red Teaming Pre-Deployment

After an AI application is created, it needs to be thoroughly tested. This is beyond quality control in the old sense. ‘AI red teaming’ is a critical and necessary practice where security experts (or their algorithms) actively try to break the AI. They act like adversarial attacks, trying to circumvent safety filters, provoke biased outputs, or cause the model to leak data.

Automated red teaming provides a scalable approach to conducting continuous dynamic testing across numerous AI applications, including commercial, open-source, and even custom, fine-tuned ones. This type of gamified exercise can identify flaws in a lab setting, so that developers can shore up defenses before pushing the app to production. It is a founding principle in today’s AI security architectures.

Protection at RuntimeRuntime

The target of an installed AI application. Runtime protection is the last line of defence, and it’s the only sane pole. This requires the ability to monitor all interactions with AI in real time and block these threats as soon as they happen. Proper runtime security can help prevent prompt injections, jailbreak attacks, or data theft attempts.

Having built-in guardrails helps us to enforce compliance and security policies. For example, a guardrail can prevent the AI from processing or showing personal identifying information (PII) that would violate regulations such as GDPR. With real-time monitoring, detection, and response, you gain visibility and control to protect against even the most sophisticated threats so that your applications remain safe and rights for both Stage 2 is Safe! This is where an end-to-end solution like the one delivered by Noma Security can be highly beneficial, integrating these disparate security layers.

Aligning with Established Security Frameworks

Many of these have emerged over time as the AI security field has matured. Following these standards will ensure security measures are both thorough and effective. Using frameworks such as the OWASP Top 10 for large language model applications, MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems), and the NIST AI Risk Management Framework (RMF), risks can be identified and addressed systematically.

These groups create a shared language and establish best practices for security and development teams. The OWASP Top 10 for large language models (LLMs) identifies serious risks, such as Prompt Injection and Insecure Output Handling. If you follow these guidelines, organizations will apply the suitable controls for your AI applications. Using a security tool already mapped to these frameworks enables teams to operationalize strong controls more effectively, ensuring application protection against widespread, severe attacks. Noma Security and other companies working in the field are crafting their solutions in accordance with these global standards.

The aim is to integrate Security Operations into Development processes (DevSecOps). When you connect and automate your existing CI/CD pipeline with AI security testing and monitoring, it becomes an enabler of innovation rather than a drawback. Through this platform, developers can quickly create and deploy AI applications securely.

Final Analysis

Trustworthy AI applications are not only a technical challenge but also a business necessity. The trust of users, partners, and regulators rests on an organization’s ability to prove that its AI is safe, dependable, and secure. And the more that AI becomes part of our daily lives, the more dangerous security failures will become.

It’s no longer enough to be responsive; you need a proactive security posture. Organizations need to take a proactive, end-to-end approach by building security into the entire AI lifecycle. This is about hardening the supply chain, testing applications in adversarial ways, and putting down strong runtime defenses. Standardization using proven models such as OWASP’s MAST and WSTG can help organizations develop robust security programs grounded in best practices.

Eventually, the way to make AI safe is collaboration between dev, sec, and ops teams—enabled by advanced tools built for this modern context. Businesses that implement a proactive security strategy and engage trusted specialists such as Noma Security will not only protect themselves from major risks but also create the conditions to unleash the power of artificial intelligence.