GenAI and LLMs behave differently from traditional applications, so they introduce a distinct set of risks that standard controls alone don’t fully cover.

On top of good cyber hygiene (patching, MFA, role-based access, network segmentation), AI workloads require you to think about:

• Data-centric threats – such as training data poisoning, dataset manipulation, and dataset reconstruction, where attackers try to corrupt or reverse-engineer the data that shapes your models.
• Model-focused attacks – including model theft or manipulation, prompt injection, and system prompt leakage, which can change how the model behaves or expose its internal logic.
• Input and output risks – like sensitive information disclosure, unsafe or biased responses, misinformation, and overdependence on model output for critical decisions.
• Supply chain exposure – vulnerabilities in pre-trained models, third-party adapters, GPU hardware, data annotations, and ML software components that may be compromised before you ever deploy them.

As GenAI becomes embedded in core workflows, these risks can lead to data leaks, regulatory issues, and operational disruption. The goal is to reimagine security around how AI actually works: understanding its data flows, model lifecycle, and integration points, then layering controls and monitoring across that full lifecycle.

Vendors like Dell focus on helping enterprises use their existing cybersecurity stack (e.g., MDR/XDR/SIEM, identity, encryption, segmentation) and complement it with AI-specific practices so that security supports, rather than slows, AI-driven innovation.

Prompt injection and sensitive information disclosure are two of the most visible risks when you expose GenAI to end users. A practical mitigation approach combines input controls, access controls, and continuous monitoring.

To reduce prompt injection risk, you can:

• Sanitize and validate inputs: Screen user prompts to remove harmful or unexpected content; use normalization and encoding to limit misuse.
• Use NLP/ML-based detection: Apply models to flag manipulated or malicious prompts before they reach the core LLM.
• Constrain outputs: Define strict response formats and apply prompt filtering and response validation so the model stays within intended boundaries.
• Apply access controls: Use RBAC, MFA, and identity management to limit who can access high-risk capabilities and data.
• Monitor and log activity: Use MDR/XDR/SIEM to detect anomalies, unauthorized access, or data exfiltration attempts in near real time.
• Continuously validate models: Regularly check that models haven’t been tampered with and run robustness checks to uncover new weaknesses.

To prevent sensitive information disclosure, you should:

• Sanitize both inputs and outputs: Strip or mask PII and proprietary data before it reaches the model, and validate outputs for accidental disclosure.
• Use strong encryption: Techniques like homomorphic encryption can allow processing of sensitive data while it remains encrypted, reducing exposure even while in use.
• Secure data collection and storage: Enforce data protection and governance policies so that only appropriate data is ingested and retained.
• Harden APIs and interfaces: Use secure APIs, review configurations regularly, and minimize the exposed attack surface.
• Educate users and admins: Provide AI-specific security awareness training to reduce unsafe usage and accidental data sharing.

By combining these measures, you can significantly lower the likelihood that a crafted prompt will hijack your model’s behavior or that the system will leak sensitive information in its responses.

AI systems depend on a complex supply chain of hardware, software, models, and data. That creates two major categories of risk: supply chain vulnerabilities and model data poisoning.

Supply chain vulnerabilities can appear in:

• Pre-trained models and adapters – where integrity may be compromised before you download or fine-tune them.
• GPU hardware and infrastructure – which, if not validated for security, can introduce low-level attack paths.
• ML software components – libraries, frameworks, and vector stores that may contain exploitable flaws.
• Data and annotations – where tampering can alter how the model learns or behaves.

Model data poisoning occurs when attackers inject corrupt or misleading data into the training pipeline. This can happen during raw data collection, annotation, preprocessing, or via external data feeds. The impact ranges from degraded accuracy to hidden backdoors and harmful outputs.

To mitigate these risks, organizations can:

• Vet suppliers and require secure supply chain practices, including transparency into model and component origins.
• Implement a Software Bill of Materials (SBOM) to track and verify all software and model components used.
• Validate models regularly to confirm they haven’t been altered and still behave as expected before deployment.
• Run containers and pods with least privilege so that a compromise has limited blast radius.
• Deploy firewalls to block unnecessary network connectivity and reduce exposure.
• Protect data and annotations with strong access controls and integrity checks.
• Use secure, validated hardware and trusted ML software components.
• Apply secure development and configuration practices, with regular reviews, audits, and updates.
• Use anomaly detection and data validation during training to spot suspicious patterns and ensure only clean, high-quality data is used.
• Isolate fine-tuning environments and segment networks to limit access to critical training and inference components.

By treating the AI supply chain and training data pipeline as first-class security concerns, you can reshape your AI security posture and build more dependable GenAI and LLM solutions.