Artificial intelligence (AI) is transforming industries, but it also introduces new security risks that traditional security measures don’t tackle. For example, AI models can be poisoned, manipulated, stolen, or exploited through adversarial techniques or unauthorized access.
That makes AI security critical. It protects business assets that rely on AI and secures AI itself from emerging threats. This article dives into how AI threats differ from other cloud detection and response tactics and what best practices are current winners for protecting AI assets.
What is AI Security?
AI security protects AI systems, including their models and data, from threats.
It’s necessary to talk about AI security separately from other forms of cloud security because AI introduces unique threats, for instance:
- Data Poisoning: Attackers manipulate AI training data to introduce bias, degrade accuracy, or embed backdoors.
- Model Extraction: Attackers reverse-engineer an AI model by repeatedly querying it to recreate its logic or steal intellectual property.
- Prompt Injection: Malicious inputs trick AI (especially language learning models or LLMs) into bypassing safeguards or revealing sensitive data.
- Dynamic Data Risks: AI models process unpredictable real-time data, increasing exposure to adversarial inputs or bias introduction.
- Model Drift and Exploitation: AI models can unintentionally evolve due to changing inputs, leading to security vulnerabilities or unexpected decision-making errors.
But AI security must also plan for cloud threats like unauthorized access. Protecting AI therefore requires a combination of familiar tools and strategies alongside novel ones. In short, secure AI requires adversarial testing, model explainability, runtime monitoring, and strict access controls: security measures tailored to AI’s evolving, complex, and often opaque decision-making processes.
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Key Components of AI Security
In mid-2024, business consultancy McKinsey estimated that 65% of companies regularly used AI, a figure that had doubled from just ten months previously.
Today, 34% of businesses in the US, EU, and China not only use public AI models, but have already deployed their own AI, with 39% more actively exploring AI their own AI projects and auxiliary services like “AI Clouds” emerging to serve them.
And security for AI is paramount.
Further, AI security isn’t just about protecting AI models, it’s also about ensuring AI-driven systems operate securely, reliably, and comply with enterprise security policies. However, traditional security controls can fall short in AI environments due to dynamic data flows, complex model behavior, and AI’s reliance on third-party components.
Let’s look at the key components of AI models where specialized security is a must.
AI Data Security: Protecting Sensitive Training and Inference Data
AI models thrive on massive datasets, but exposed or misconfigured cloud storage can lead to data leaks, compliance violations, or model theft. Securing AI data requires:
- End-to-end encryption for AI training and inference data.
- Real-time monitoring of AI-related data flows in cloud environments.
- Access control policies so only authorized identities (machine or human) interact with sensitive datasets.
AI Model Integrity: Detecting Manipulation and Poisoning Attempts
Threat actors tamper with AI models through data poisoning, model inversion attacks, and adversarial inputs. Without continuous integrity checks, security teams may not detect subtle model manipulations that impact AI-driven decision-making.
- Runtime model integrity verification ensures models operate as intended.
- Threat detection mechanisms identify anomalies in AI decision patterns.
- Version control & rollback strategies allow rapid response to model manipulation.
AI Identity & API Security: Managing AI Service Access Risks
AI systems often interact with external APIs, cloud services, and third-party integrations, creating identity risks that extend beyond traditional IAM models. Attackers compromise API keys, service accounts, or machine identities to manipulate AI-driven applications.
- Continuous API security monitoring detects unauthorized interactions.
- Zero Trust for AI identities enforces strict access policies.
- Real-time threat detection prevents credential misuse in AI pipelines.
Protecting AI Model Integrity: Actionable Strategies
Beyond eyeing attractive AI data, attackers are actively developing ways to manipulate how AI models learn, infer, and behave in real-world conditions Their manipulatability means that, unlike traditional applications, AI models can be subtly compromised through training data, inference inputs, and model access patterns — without triggering standard security alerts.
AI security will include securing data pipelines, APIs, and cloud workloads that power AI, while also implementing continuous validation mechanisms to detect subtle integrity breaches before they escalate.
The table below outlines key AI integrity risks and how they manifest, along with fundamental steps to set the stage for proactive AI model security.
| AI Security Risk | What is it? | Key Security Steps |
| Data Poisoning | Attackers inject manipulated or mislabeled data into AI training datasets to skew model behavior. | Implement strict data validation before ingestion Use differential privacy Restrict access to training data repositories with Zero Trust principles. |
| Model Extraction (Inversion Attacks) | Attackers repeatedly query AI models to infer training data or recreate proprietary logic. | Monitor API interaction patterns to detect excessive queries Apply rate limiting & query obfuscation Use federated learning or homomorphic encryption to minimize training data exposure |
| Prompt Injection (LLM Exploits) | Malicious inputs manipulate LLMs into bypassing safeguards, leaking data, or generating harmful outputs. | Sanitize and tokenize user inputs before AI processing Implement strict content filtering Use fine-tuned instruction adherence to reinforce AI compliance |
| Model Drift & Concept Drift | AI models degrade over time as new data distributions shift, leading to inaccurate or biased decisions. | Deploy continuous model monitoring for drift detection Implement scheduled retraining with verified datasets Establish version rollback mechanisms to revert to known-safe models. |
| Adversarial Inputs | Attackers manipulate AI inference with subtly altered inputs that force incorrect predictions. | Use adversarial testing (red teaming) to simulate attacks before deployment Apply robust feature masking to prevent small input perturbations from affecting decisions Train models with adversarial defense techniques like input denoising |
| Unauthorized Model Access | AI models stored in cloud environments may be accessed, copied, or modified by unauthorized users. | Enforce identity-based access control (IAM for AI models) Apply runtime monitoring for unauthorized access attempts Use confidential computing to protect models in use |
AI Security Architecture and Implementation
Teams must also commit to securing the broader underlying infrastructure that runs AI workloads. Even a well-protected model can be compromised if the cloud environments, APIs, and execution pipelines it relies on are vulnerable.
To build resilient AI security, harden AI environments, enforce workload isolation, and prevent data leakage or unauthorized manipulation at runtime. These components of a security posture checklist built for AI workloads are foundational.
Tenant Isolation Frameworks
AI models often operate in multi-tenant environments, whether running on shared cloud infrastructure or leveraging external APIs for data enrichment. Without strong isolation mechanisms, sensitive AI workloads risk data leakage, cross-tenant access risks, and unauthorized model manipulation.
A good tenant isolation framework should:
- Use VPC segmentation and namespace isolation to ensure AI workloads are restricted to their designated environments.
- Enforce strict role-based and identity-based policies to prevent unauthorized cross-tenant access.
- Implement dedicated compute instances for AI workloads handling high-risk or regulated data, reducing the attack surface from shared resources.
Sandboxing and Environment Controls
Because AI models process unpredictable, unstructured, and often user-supplied data, attackers exploit AI input mechanisms to trigger malicious behavior through adversarial perturbations, payload injection, or model inference abuse. Without proper execution controls, AI workloads become an attack vector rather than a security asset.
Effective sandboxing and AI environment controls should include:
- Ephemeral execution environments that prevent persistent tampering or unauthorized persistence in AI model execution.
- Containerized AI inference to ensure each request runs in a controlled, auditable, and resource-limited environment.
- Strict egress filtering to prevent AI models from leaking sensitive data through indirect output manipulation.
One way of viewing things from an AI security standpoint is AI workloads should be treated like untrusted user applications. This means they need to be sandboxed, monitored, and isolated from sensitive backend systems to prevent unintended data exposure.
Input Sanitization and Validation
AI security starts at the input layer. AI models consume massive amounts of data that are structured and unstructured, human-generated and machine-generated. Attackers exploit unfiltered inputs to manipulate model behavior, inject bias, or extract unintended outputs.
An effective input security strategy should:
- Implement structured validation for AI-generated and user-supplied inputs, so that models only process expected data types.
- Enforce token and prompt sanitization to neutralize adversarial inputs before they reach the AI model.
- Apply real-time anomaly detection to identify malicious payloads attempting to manipulate AI inference logic.
From Tactical to Strategic: Governance, Compliance, and Integration
As AI security threats evolve, teams won’t be able to rely on isolated AI security controls alone. AI security must be embedded into broader cybersecurity strategies, aligning with governance frameworks, risk management, and compliance policies.
That will mean integrating AI security into Zero Trust models, enterprise risk assessments, and compliance workflows so that AI workloads follow the same security rigor as traditional IT assets.
Here are some key tips on how to structure AI security programs, from aligning with compliance mandates to enforcing AI-aware security policies in real-world environments.
Embedding AI Security into Enterprise Security Strategies
A resilient AI security program must integrate into existing security frameworks, aligning with Zero Trust, Identity and Access Management (IAM), and cloud security principles.
Key Actions:
- Mapping AI security to established frameworks like NIST AI RMF, MITRE ATLAS, and ISO/IEC 27001 to ensure a structured approach.
- Embedding AI risk assessment into broader cybersecurity workflows, ensuring AI workloads follow the same security rigor as traditional IT assets.
- Adopting Zero Trust principles for AI systems, enforcing continuous authentication, strict least-privilege access, and micro-segmentation for AI-driven services.
Avoiding Regulatory Pitfalls
AI security must align with compliance mandates that govern data privacy, model explainability, and risk management. Regulators are increasingly scrutinizing how AI systems process sensitive data, make decisions, and ensure fairness.
Key Actions:
- Ensuring AI model governance aligns with regulations like GDPR, HIPAA, and emerging AI laws (e.g., the EU AI Act).
- Implementing model explainability and auditability, ensuring AI-driven decisions are transparent and reproducible for regulatory scrutiny.
- Enforcing strict data residency and encryption policies to prevent AI-related compliance violations.
Mapping AI-Specific Risks
AI introduces new risk factors that traditional risk assessment methodologies fail to capture. A strong AI security program must identify, quantify, and mitigate risks related to data poisoning, model theft, and adversarial attacks.
Key Actions:
- Developing an AI threat modeling framework, mapping attack vectors using MITRE ATLAS.
- Implementing AI-specific risk scoring models, prioritizing threats based on model sensitivity, data exposure, and adversarial manipulation risks.
- Applying adversarial testing (e.g., red teaming AI models) to simulate real-world attacks and identify vulnerabilities before they’re exploited.
AI Threat Detection at Runtime
Traditional SIEM tools often lack visibility into AI-driven workloads, necessitating AI-specific security monitoring. AI security doesn’t stop once a model is trained and deployed. Threats can emerge at runtime, during inference, API interactions, and data processing.
Key Actions:
- Implementing real-time AI threat intelligence, tracking emerging AI-specific attack patterns in dark web forums and security research.
- Monitoring AI API interactions and analyzing input patterns, access behaviors, and privilege escalation attempts at the API level.
- Enforcing policy-driven runtime security and automatically blocking unauthorized AI interactions or adversarial inputs in real time.
- Correlating AI security events with broader cloud telemetry to clarify how AI-related threats connect to identity abuse, API compromises, and data exfiltration attempts.
Upwind Strengthens AI Security Posture
AI security demands continuous visibility, real-time risk assessment, and adaptive security controls that can keep pace with AI’s evolving attack surface.
Upwind’s CNAPP provides the AI-aware runtime protection necessary to secure AI-driven workloads, like:
- Continually monitoring AI workloads at runtime, detecting anomalous behavior with its own machine learning prowess, detecting unauthorized API interactions and adversarial model manipulation attempts.
- Enforcing policies in real time for AI inference environments to prevent unauthorized API interactions and runtime exploits.
- Continuously monitoring and enforcing AI security policies without manual intervention.
- Ensuring compliance and governance for AI-driven workloads in cloud environments.
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FAQ
Is AI Security About Securing AI or Using AI to Secure Business Assets?
Most typically, “AI Security” refers to the practice of securing AI models.
But while teams will increasingly need to protect AI assets and models, they’ll also need to learn how to use AI security to secure AI (and other business assets). After all, the speed at which vast amounts of data can be parsed, examined, and used to identify patterns is useful in tracking attack patterns for cybersecurity as much as other business functions.
A resilient AI security program must be proactive, adaptive, and, ultimately, use the same powerful tools that attackers use against it. Using AI for security most commonly includes:
1. Threat Detection and Response
2. Security Automation and Orchestration
3. Rapid Incident Response Mitigation
How do you prevent prompt injection attacks?
Prevent prompt injection attacks with strict input validation, access controls, and AI-specific security measures to ensure models do not process or reveal unintended outputs. Those include:
- Sanitizing Inputs: Strip or neutralize harmful instructions before the model processes them.
- Using Context-Aware Filtering: Detect and block manipulative prompt patterns, especially in LLMs.
- Implementing User & API Access Controls: Enforce Zero Trust principles for AI interactions, limiting who can issue prompts.
- Using Fine-Tuned Model Instructions: Reinforce strict adherence to predefined behavior, reducing deviation risks.
- Instituting Output Validation & Logging: Monitor AI responses for signs of manipulation or unexpected disclosure.
Additionally, rate limiting and adversarial testing help detect and prevent coordinated prompt-based attacks before exploitation occurs.
What role does AI play in modern security operations?
AI plays a key role in modern security operations (for securing both AI and non-AI assets) by improving threat detection, allowing for advanced automation, and reducing response speed. Key roles of AI and machine learning include:
- Real-Time Threat Detection: AI analyzes vast amounts of security telemetry to identify anomalous patterns, insider threats, and emerging attack vectors faster than rule-based systems.
- Security Automation & Orchestration: CNAPPs with machine learning can streamline security workflows by reducing false positives, triaging alerts, and executing automated response actions.
- Adaptive Incident Response: AI predicts attack progression, prioritizes risks dynamically, and assists in digital forensics to accelerate mean-time-to-resolution (MTTR).
- AI vs. AI Defense: Attackers are weaponizing AI for deepfake phishing, automated exploits, and social engineering, so organizations must fight AI with AI to stay ahead.
In short, AI enables proactive, data-driven, and automated security defenses, reducing analyst workload while improving response accuracy.
How can organizations secure their AI models?
Securing AI models starts with protecting the data they learn from. Training data should be carefully validated to prevent poisoning, and access to datasets must be tightly controlled. Without strong safeguards, attackers can subtly manipulate AI decision-making by introducing biased or misleading inputs. Organizations should also apply adversarial testing, essentially stress-testing models against potential attacks, to ensure they remain resilient under real-world conditions.
Beyond data, access control and monitoring are also foundational for securing models. AI models should be protected with strict authentication, role-based access, and API security to prevent unauthorized use or extraction. Once deployed, continuous monitoring helps detect anomalies, like unusual query patterns or adversarial inputs.
Overall, AI security is an ongoing process of refinement, auditing, and adaptation to keep up with evolving threats.