AI Security Solutions: How Businesses Protect Models, Data, and LLM Applications

Anna Ivakhnik

Artificial intelligence has moved from research labs into core business operations and consumer‑facing products, ushering in powerful new capabilities and entirely new attack surfaces. Traditional application security focused on protecting code, infrastructure, and data. AI expands this perimeter to include the models themselves, their training data, the prompts used to invoke them, retrieval layers, agents that act on behalf of users, and the orchestration pipelines that glue everything together. Each component introduces unique vulnerabilities: models can leak training secrets or be coerced into disclosing data, malicious prompts can instruct an agent to bypass controls, and insecure plugins or API endpoints can expose sensitive functions. Organisations therefore need AI security solutions that protect not just applications and databases but the models, contexts and pipelines powering today’s generative systems.

What AI security solutions actually cover

At their best, AI security solutions provide a coherent stack of controls spanning the entire lifecycle of AI systems. These solutions include safeguards for:

• Data – ensuring quality, provenance, lineage, confidentiality and integrity of datasets used to train, fine‑tune and feed AI models. Access boundaries and encryption are crucial to prevent leakage and misuse.
• Models – protecting intellectual property, resisting tampering, enforcing versioning and provenance, and monitoring model behaviour over time.
• Prompts and outputs – validating and sanitising prompts, hardening system prompts against prompt‑injection attacks, and filtering model outputs before they reach downstream systems.
• APIs and plugins – controlling third‑party extensions and plugin calls, minimising permissions and functionality, and enforcing rate‑limiting to prevent denial‑of‑service and cost‑exhaustion attacks.
• Identities and permissions – applying least‑privilege principles to agents and users, segregating identities between the application and model, and enforcing multi‑factor authentication for sensitive operations.
• Infrastructure – securing MLOps pipelines, container registries, build systems, deployment environments, and ensuring secrets management across CI/CD.
• Monitoring and governance – instrumenting logging, anomaly detection and audit trails across the AI stack, linking them to risk registers and incident response plans.

Unlike simple perimeter tools, these solutions function together as a system of internal controls. Many vendors offer point products that promise to “secure AI”, yet enterprises benefit most when adopting a layered approach tailored to their technology stack and regulatory obligations. Intersog’s own AI security services emphasise this integrated perspective, combining secure development practices with model and data governance.

The threat landscape enterprises now face

Generative AI and large language models (LLMs) introduce attack patterns that do not map neatly onto traditional software vulnerabilities. The 2025 OWASP Top 10 for LLM applications highlights some of the most significant risks.

Prompt injection occurs when malicious prompts alter a model’s behaviour or output. OWASP notes that prompts do not need to be visible to users and can be hidden in external content; injection attacks may cause models to violate guidelines, generate harmful content, leak sensitive information or execute commands.

Insecure output handling arises when applications trust model outputs without sanitisation. Insufficient validation or encoding can lead to cross‑site scripting (XSS), server‑side request forgery or privilege escalation. A model’s output should be treated like input from an untrusted user: filtered, encoded for context and logged for anomalies.

Training data poisoning attacks compromise data pipelines, inserting malicious or biased data that causes models to behave incorrectly. The OWASP document warns that attackers may bias outputs by manipulating training data, insert backdoors that trigger hidden behaviours, or exploit prompt‑injection to influence data ingestion.

Model denial‑of‑service (DoS) expands beyond simple throughput exhaustion. OWASP describes “Unbounded Consumption” attacks where adversaries send variable‑length inputs or heavy queries that exhaust resources, trigger costly API calls or replicate models via API extraction. This can lead to denial of wallet (DoW) conditions in pay‑per‑use cloud models.

Other risk groups include:

• Supply chain vulnerabilities – malicious or compromised open‑source models and libraries, insecure container registries or packaging tools and unverified third‑party plugins.
• Adversarial manipulation – adversaries craft inputs to trick classifiers or generative models, degrade performance or bypass safety constraints. MITRE’s ATLAS PoisonGPT case study shows how attackers can download an open‑source model, modify its weights to embed false facts and upload it back to a public model hub, enabling widespread distribution of poisoned models.
• Shadow AI and unauthorised model use – employees or business units deploy unsanctioned models or call external APIs without proper review, bypassing corporate governance and compliance.
• Data leakage through retrieval and agent workflows – retrieval‑augmented generation (RAG) pipelines may inadvertently expose private data from knowledge bases; agents with excessive autonomy or plugin permissions might exfiltrate secrets or perform unintended actions.
• Weak access control around internal AI tools – misconfigured APIs, insecure job schedulers (as in the “ShadowRay” attack on Ray jobs) or over‑privileged service accounts can grant adversaries control over AI workloads.

These threats demonstrate why traditional application security alone is insufficient. AI systems are dynamic, data‑driven and context‑dependent; their attack surfaces encompass data flows, human‑model interaction and complex supply chains. Addressing the risks requires a comprehensive strategy across governance, data, model design, runtime and testing.

The core layers of an effective AI security strategy

4.1 Governance and risk management

Security for AI starts with governance. The U.S. National Institute of Standards and Technology (NIST) emphasises that its AI Risk Management Framework (AI RMF) is voluntary, rights‑preserving and adaptable across sectors. It divides risk management into four functions - Govern, Map, Measure and Manage - with the Govern function cutting across the lifecycleatasets. Organisations should classify AI systems by criticality, map model usage to business processes, and document acceptable‑use rules. Risk appetite should be established and aligned with regulatory requirements such as the EU AI Act, which imposes strict obligations on high‑risk systems including risk assessment, dataset quality controls, logging, documentation and human oversight. A central governance team should oversee compliance, maintain a registry of models, data sources and third‑party dependencies, and drive periodic audits.

4.2 Data and model protection

Protecting data and models is foundational. ENISA’s report on securing machine‑learning algorithms highlights that traditional security controls must be supplemented with techniques tailored to ML, such as including adversarial examples in training datasets and monitoring model vulnerabilities. Controls include:

• Data quality and lineage – verify provenance of training and fine‑tuning datasets, enforce data minimisation and remove personal or sensitive information to reduce leakage risk. Maintain lineage metadata so that any issues can be traced back to source.
• Encryption and secret management – encrypt data in transit and at rest; protect API keys and tokens used by models and retrieval services. For retrieval‑augmented applications, isolate knowledge bases and implement row‑level permissions.
• Access boundaries – segment access between training data, models and downstream applications. Use role‑based access control and attribute‑based policies to restrict who can view, modify or deploy models.
• Model provenance and versioning – record how a model was trained (datasets, hyperparameters, architecture), maintain signed hashes for each version and perform authenticity checks before deployment to detect tampering.
• Model privacy – implement differential privacy or synthetic data techniques to reduce memorisation of sensitive information, especially for generative models trained on proprietary datasets.

These controls help mitigate data poisoning and intellectual property theft, and they support regulatory compliance by providing audit trails. For implementation support, enterprises often draw on cybersecurity services and data science and machine learning services partners to build robust pipelines.

4.3 Application and LLM security controls

Application‑level defences focus on controlling inputs, prompts, outputs and interactions with third‑party systems. The OWASP Top 10 provides specific countermeasures:

• Input validation and prompt hardening – define system prompts with explicit instructions about the model’s role, capabilities and limitations; constrain context windows and instruct the model to ignore attempts to override its instructions. Validate and sanitise user inputs using deterministic code or pattern matching before passing them to the model, and separate untrusted content within RAG pipelines.
• Output filtering and encoding – treat model responses as untrusted; enforce zero‑trust principles by validating outputs before they trigger downstream actions. Encode outputs appropriately (e.g., HTML encoding for web content, SQL escaping for queries) and use prepared statements to avoid injection. Implement semantic filters and rate‑limit high‑risk functions to reduce the blast radius.
• Guardrails for retrieval and tool usage – clearly mark external content retrieved via RAG; restrict which functions agents can call; limit plugin functionality and permissions to the minimum necessary. Avoid open‑ended extensions that can execute arbitrary commands. Provide human‑in‑the‑loop approval for high‑impact actions.
• Rate limiting and unbounded consumption controls – implement quotas, timeouts and request size limits to protect against variable‑length input floods, DoW attacks and model extraction. Obfuscate sensitive probabilities (logits) and restrict access to internal model details.

These measures should be embedded into application code and the orchestration layer, with continuous pen‑testing and adversarial simulations to validate effectiveness.

4.4 Infrastructure, MLOps and runtime security

Security must extend into deployment pipelines and runtime environments. The infrastructure layer includes CI/CD systems, container registries, orchestration tools (Kubernetes, serverless platforms) and runtime monitoring. Key practices include:

• Secure deployment pipelines – apply signed artifacts, automated scanning and policy enforcement in build pipelines. Verify dependencies, container images and model artifacts to detect tampering or supply‑chain attacks. Use infrastructure‑as‑code scanning and vulnerability management.
• Environment separation and container security – separate development, testing and production environments; isolate model serving containers; and restrict network access. Use container runtime security to detect anomalies such as unusual system calls or binary execution. Manage secrets securely through dedicated vaults.
• Model monitoring and anomaly detection – instrument runtime metrics to detect drift, unusual output patterns or surges in resource consumption. Monitor for prompt injection attempts, data exfiltration or unexpected API calls. Tools like MITRE’s ATLAS emphasise red teaming and case studies such as PoisonGPT to simulate real‑world attacks. Integrate monitoring with incident response playbooks.
• Rollback plans and disaster recovery – maintain the ability to revert to previous model versions or fallback heuristics if anomalies are detected. Test rollback procedures regularly.

Reliable infrastructure and MLOps practices rely on robust DevOps expertise; organisations often leverage cloud and DevOps services to harden these layers.

4.5 Testing, red teaming and continuous monitoring

Continuous evaluation is essential because AI systems evolve rapidly. Testing should include:

• Automated evaluations – use benchmark datasets and scenario‑based tests to evaluate accuracy, fairness and adversarial robustness. Regularly run unit tests on prompts, RAG pipelines and agent behaviours.
• Attack simulations and adversarial testing – perform red‑team exercises against models and pipelines. The MITRE ATLAS knowledge base provides real‑world tactics and techniques (e.g., model tampering, prompt injection, supply chain compromise) that can be used to design adversarial tests. Structured red‑teaming helps uncover unknown vulnerabilities.
• Incident response readiness – develop playbooks for AI‑specific incidents, including procedures to isolate compromised models, rotate keys, revoke plugins and notify stakeholders. Ensure logs provide sufficient evidence for forensic analysis.
• Continuous monitoring – implement telemetry across data pipelines, model outputs, infrastructure and user interactions. Use anomaly detection and correlation to identify suspicious patterns. Feed results back into governance and risk assessments.

How to evaluate AI security solutions

Selecting the right AI security solution requires assessing how well it covers the entire AI lifecycle and fits into existing security and DevOps processes. Decision‑makers should consider:

1. Lifecycle coverage – Does the solution address data ingestion, model development, deployment, runtime monitoring and decommissioning? Can it detect and prevent the risks outlined by OWASP, MITRE and ENISA?
2. Compatibility – Does it integrate with your cloud provider, data platforms, MLOps pipelines and security tooling? Solutions should support API‑based integration and run in SaaS, virtual private cloud (VPC) or on‑premises environments.
3. Support for LLM, RAG and agentic workflows – AI security products must understand modern architectures, including retrieval‑augmented generation, plugin ecosystems and agent frameworks. Look for features that harden prompts, isolate tools and monitor cross‑modal interactions.
4. Observability and logging – A robust product should provide granular logs for prompts, outputs, API calls, and resource usage. It should integrate with SIEM and SOC workflows and enable anomaly detection.
5. Policy enforcement and human oversight – Solutions should allow fine‑grained policies (e.g., blocking certain prompts, restricting plugin calls) and support human‑in‑the‑loop mechanisms for high‑risk actions.
6. Compliance mapping – Ensure the product helps demonstrate compliance with frameworks such as NIST’s AI RMF, the EU AI Act, ISO/IEC 42001 and sector‑specific regulations (e.g., GDPR, HIPAA). Look for built‑in risk assessment templates and reporting.
7. Vendor transparency and trust – Evaluate how vendors secure their own models and data, whether they support third‑party audits, and whether they keep pace with evolving standards. Avoid black‑box solutions that cannot explain how controls work.
8. Deployment flexibility – Choose solutions that can be deployed as SaaS, self‑hosted within a VPC or on‑premises to meet data residency and security requirements. This flexibility is critical for regulated industries.

Real business use cases

Financial services. Banks and fintech companies leverage generative AI for customer support, fraud detection and document processing. However, they must protect customer data and comply with strict regulations. Models trained on transaction histories risk memorising personal information; retrieval agents may inadvertently expose account details. Leading banks now implement prompt‑hardening and output filtering, monitor models for drift and anomalies, and enforce strict access controls with human oversight. They also maintain inventories of models and data sources for audit readiness.

Healthcare and regulated industries. Healthcare providers use AI for clinical documentation, diagnosis assistance and patient engagement. Models trained on sensitive medical data must comply with data protection laws (e.g., HIPAA in the U.S., GDPR in Europe). Training data poisoning could jeopardise patient safety by producing harmful recommendations, while prompt injection might cause erroneous advice. Organisations in this sector prioritise data anonymisation, differential privacy, encryption, and robust validation of model outputs by clinicians. They adopt governance practices to classify models by risk and implement red‑teaming scenarios involving adversarial input to evaluate potential harm.

SaaS and product companies. Software vendors embed LLMs into productivity tools, chatbots and code assistants. These applications often call third‑party APIs, run in multi‑tenant clouds and interact with untrusted content. Key concerns include supply‑chain vulnerabilities, unbounded consumption, and insecure plugins. SaaS providers implement rate limiting, sandboxing, plugin whitelisting and output sanitisation to prevent misuse. They align with secure coding standards (such as OWASP ASVS) and maintain robust logging and monitoring to detect abuse. For internal use of generative models, companies also enforce policies around acceptable prompts and require employees to use approved AI services.

A practical implementation roadmap

Implementing AI security is a journey. Organisations should adopt a phased approach:

1. Immediate priorities – Conduct a comprehensive inventory of AI models, data sources, prompts, APIs and plugins. Define policies on acceptable use, prompt injection testing and data retention. Establish access boundaries by segregating environments and restricting privileges. Enable logging and basic monitoring across the AI stack.
2. Near‑term priorities – Develop guardrails for input and output validation, implement rate limiting and resource quotas, and review all third‑party models and plugins for supply‑chain risks. Integrate adversarial testing and penetration testing into the development pipeline. Enhance monitoring with anomaly detection and correlation across data, models and infrastructure. Conduct a vendor review for AI security tools that align with the evaluation framework above.
3. Longer‑term priorities – Establish a formal AI governance program aligned with NIST’s AI RMF and the EU AI Act. Mature risk classification and model lifecycle management, including versioning, provenance tracking and retirement policies. Invest in continuous red‑teaming exercises, using frameworks like MITRE ATLAS to simulate diverse attack vectors and practise incident response. Develop and rehearse incident playbooks addressing prompt injection, data leakage, poisoning and denial‑of‑service events. Over time, integrate AI security metrics into board‑level risk dashboards and adopt frameworks such as ISO/IEC 42001 (AI management system) when available.

Conclusion

AI security solutions are not a single product but a coordinated collection of controls across governance, data, models, applications and infrastructure. The NIST AI RMF underscores the importance of a risk‑based, voluntary framework applicable across sectors, while OWASP’s Top 10 reminds us that prompt injection, insecure output handling and supply‑chain risks require active mitigation. MITRE ATLAS provides a living knowledge base of real‑world adversary tactics, and ENISA emphasises the need for machine‑learning‑specific controls. Finally, the EU AI Act introduces regulatory obligations, particularly for high‑risk and general‑purpose AI models. Businesses must therefore adopt a holistic view: implement layered technical safeguards, establish governance and risk management frameworks, continuously test and monitor their systems, and stay abreast of evolving standards. By integrating AI security solutions into the broader cybersecurity and DevOps fabric, enterprises can innovate confidently while protecting people, data and models.