What Is AI Security?
AI security involves protecting AI systems and data from attacks, theft, and misuse by using a combination of technical controls, security practices, and governance. This includes securing the AI data pipeline and training environments, hardening model deployments, and defending against attacks like prompt injection. It is also used by security teams to enhance threat detection and response through AI-powered tools.
How to secure AI systems:
- Implement secure development lifecycles for AI: Implement security measures across the entire AI lifecycle, from data collection and model training to deployment and runtime.
- Apply model access controls and encryption: Apply zero trust security across all AI environments and secure training data, model artifacts, and the infrastructure where models are developed and deployed.
- Establish continuous AI risk assessment: Create a process to regularly identify and prioritize potential risks related to AI systems.
- Enforce governance and compliance monitoring: Establish an AI risk management framework and governance controls to evaluate and manage risks.
- Build multi-layer AI incident response plans: Continuously monitor AI systems for suspicious behavior and have a plan to respond to threats, including automated responses.
This is part of a series of articles about AI security.
In this article:
Adversarial Attacks and Input Manipulation
Adversarial attacks involve crafting inputs intended to fool or mislead AI models into making incorrect predictions or classifications. These manipulations are often subtle enough to evade detection by human operators but can cause significant disruption in systems such as facial recognition, image classification, or language-based AI. Attackers may introduce imperceptible pixel changes to images or insert carefully chosen words into textual prompts to achieve the desired misclassification or model behavior.
The increasing sophistication of adversarial attacks means that defensive strategies must constantly evolve. Defenses may include input validation, adversarial training, and robust anomaly detection. However, these are far from foolproof due to the adaptability of attackers. Input manipulation can also serve as a vector for more complex attacks when combined with prompt injection, data poisoning, or model extraction attempts—reinforcing the need for holistic monitoring and layered security measures.
DDoS and Unbounded Consumption Attacks
AI systems, especially those exposed via APIs or real-time inference endpoints, are vulnerable to denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks. These attacks aim to overwhelm compute resources such as GPUs, memory, or API quotas by generating excessive requests, causing degraded performance or total service outages. In AI contexts, attackers may exploit large model sizes or expensive inference costs to disproportionately tax systems using relatively simple input traffic.
Unbounded consumption attacks go further by triggering resource-intensive model behavior through specially crafted inputs. For example, attackers may exploit a generative model’s tendency to produce long outputs or activate complex reasoning chains, consuming CPU/GPU time, I/O bandwidth, or memory far beyond normal use. Without rate limiting, billing controls, or usage segmentation, such attacks can escalate operational costs and degrade service for legitimate users.
Prompt Injection
Prompt injection is a unique threat for large language models and conversational AI, where attackers embed malicious input directly into prompts or conversations. This can manipulate the AI into leaking data, violating access controls, or producing harmful, biased, or otherwise unintended outputs. These attacks may bypass content filtering or context-awareness safeguards by exploiting the open-ended and generative nature of modern AI models.
Preventing prompt injection requires ongoing prompt sanitization, prompt engineering best practices, and contextual integrity checks. AI systems must be designed to detect anomalous or contextually inappropriate prompts in real time. The rapid evolution in prompt design—driven by new LLM architectures and complex multi-modal models—means that prompt injection defenses must be continuously updated and rigorously tested in production environments.
Data Poisoning and Model Corruption
Data poisoning is the deliberate manipulation of training datasets to degrade model accuracy or embed backdoors, causing models to behave incorrectly or maliciously. Poisoning attacks can be carried out at many stages, including data collection, annotation, or preprocessing, making detection difficult, especially in organizations lacking rigorous data lineage and supply chain controls.
Model corruption goes beyond poisoning, encompassing any unauthorized alteration to model weights, architecture, or functional logic after deployment. Malicious updates or compromised CI/CD pipelines can result in subtle, undetectable yet dangerous changes in model behavior. Both data poisoning and model corruption can undermine entire AI-driven processes, resulting in biased outcomes, privacy breaches, or system-wide failures.
Supply Chain and Model Drift Risks
AI systems depend heavily on open-source libraries, pretrained models, and third-party code, introducing risks across the software supply chain. Attackers might insert vulnerabilities intentionally (supply chain attacks) or exploit loosely vetted dependencies. Compromised model registries, libraries, or hardware accelerators can allow threat actors to infiltrate systems before the AI even reaches production.
Model drift is a risk unique to machine learning systems. Over time, shifts in the operational environment or data distributions can cause previously robust models to behave unpredictably—a problem known as concept drift or data drift. Failure to monitor for drift compromises both the security and reliability of AI, as subtle, long-term changes go undetected, eroding the intended safety and accuracy guarantees.
Secure by Design for AI Pipelines
Embedding security into AI pipelines from the outset is critical. This means integrating threat modeling, data validation, and secure coding practices into every phase of model development. Secure-by-design principles also include rigorous management of access controls, encryption of data at rest and in transit, and comprehensive auditing of any component or dependency included in the pipeline.
Incorporating security checks into CI/CD workflows and regularly reviewing third-party libraries or pretrained models further reduces the attack surface. Continuous integration of automated vulnerability scanning and penetration testing ensures that the pipeline itself does not become a vector for exploitation. Building and enforcing repeatable, documented processes ensures a consistent security posture as models move from development to deployment.
Model Explainability and Transparency
Explainability is essential for effective AI security. Understanding how and why a model makes its predictions allows organizations to detect unusual behavior, audit outputs for bias or manipulation, and build user trust. Model transparency—including the documentation of training data sources, preprocessing steps, and hyperparameter tuning—also supports regulatory compliance and incident response.
Transparency mechanisms, such as model cards and audit trails, should be embedded throughout the AI lifecycle. This documentation is not just useful for compliance audits but also serves as an early warning system for unexpected model drift, adversarial interference, or system misuse. Openness about AI decision-making processes makes it harder for attackers to exploit black-box vulnerabilities.
Privacy Preservation and Differential Privacy
AI models frequently rely on sensitive personal or proprietary data, so privacy preservation is a core security requirement. Approaches such as differential privacy enable organizations to share aggregate insights while minimizing individual data exposure. By introducing controlled noise or limiting the granularity of output, differential privacy techniques prevent attackers from inferring specific records from model predictions.
Beyond differential privacy, AI pipelines should enforce data minimization, anonymization, and granular access controls. Care must be taken to monitor downstream outputs for privacy leakage, especially in generative AI systems where outputs might inadvertently echo sensitive data from training sets. Ongoing privacy risk assessments, combined with robust technical and organizational measures, are crucial for trustworthy AI deployments.
Ethical and Regulatory Alignment
With global interest in AI regulation growing, securing AI systems involves staying aligned with ethical guidelines and regulatory requirements. Frameworks like the EU AI Act and guidelines from organizations such as IEEE and NIST set minimum standards for fairness, transparency, and security. Organizations must proactively incorporate these benchmarks into technical and operational practices.
Alignment goes beyond compliance; it supports organizational reputation, builds public trust, and reduces legal liability in the event of an incident. Regular reviews of model performance for bias and fairness, transparent impact assessments, and externally auditable processes help ensure that AI systems consistently meet both ethical norms and the letter of the law.
Uri Dorot
Uri Dorot is a senior product marketing manager at Radware, specializing in application protection solutions, service and trends. With a deep understanding of the cyber threat landscape, Uri helps companies bridge the gap between complex cybersecurity concepts and real-world outcomes.
Tips from the Expert:
In my experience, here are tips that can help you better secure AI systems beyond what's covered in the article:
1. Establish AI model fingerprinting and watermarking: Embed unique identifiers or behavioral signatures into your models to detect cloning, exfiltration, or unauthorized reuse. This helps trace model leaks, identify stolen intellectual property, and prove provenance in legal disputes.
2. Conduct red team simulations specifically tailored for AI: Regularly run adversarial exercises with red teams trained in AI-specific attack tactics such as prompt leakage, API abuse, and supply chain backdooring, to uncover real-world exploit paths not found through static testing or automated scans.
3. Use honey prompts and trap inputs in LLM environments: Deploy bait prompts or decoy model behaviors to detect prompt injection attempts, input probing, or attempts to reverse-engineer context windows. Log and analyze how these are triggered to inform mitigation strategies.
4. Perform shadow inference to monitor drift and poisoning: Continuously evaluate incoming data and predictions using a secondary “shadow” model trained on verified clean data. Differences in predictions can signal concept drift or malicious data tampering before it impacts production.
5. Adopt policy-as-code for AI governance: Encode your AI governance policies—like data usage constraints, model fairness thresholds, or geographic training restrictions—into version-controlled, enforceable code. This enables continuous compliance checks and integration with CI/CD workflows.
AI security covers a broad spectrum, encompassing models of all types—traditional machine learning, neural networks, reinforcement learning, and more. It focuses on the general protection of the entire AI lifecycle: data collection, model development, deployment, and post-production monitoring. This includes mitigating threats such as data poisoning, adversarial attacks, and supply chain risks, regardless of the specific architecture or application.
LLM (large language model) security is a specialized subset, dealing with risks unique to the scale, complexity, and usage patterns of language models like GPT-5 or Gemini. Issues such as prompt injection, context leakage, content manipulation, and emergent model behaviors require targeted controls and dedicated monitoring. While some risk categories and remedies overlap with broader AI security, LLMs introduce new vectors and challenges demanding tailored defenses.
NIST AI RMF and ISO/IEC Guidelines
The NIST AI Risk Management Framework (AI RMF) provides structured guidance for identifying, assessing, and managing AI risks throughout their lifecycle. It emphasizes core functions such as map, measure, manage, and govern, and encourages organizations to embed security considerations alongside broader concerns like privacy and fairness. This framework is widely referenced in industry and forms the basis for many sector-specific best practices.
ISO/IEC provides complementary global standards such as ISO/IEC 27001 for information security management and ISO/IEC 23894 for AI risk management. These standards prescribe policy, process, and technical controls for secure AI system development and operation. Adhering to NIST and ISO/IEC guidelines helps organizations meet compliance mandates and align with industry-recognized risk management procedures.
Secure AI Framework (SAIF)
The Secure AI Framework (SAIF) is a security reference model created specifically to address unique vulnerabilities in AI/ML ecosystems. SAIF outlines security controls and monitoring approaches for different stages of the AI pipeline, including data sourcing, feature engineering, model training, and deployment environments. It places strong emphasis on secure integration of external resources and continuous system monitoring.
Adopting SAIF supports organizations in implementing layered defenses and verifying compliance with both internal policies and external regulations. The framework stresses the importance of traceability and incident response planning, ensuring that any deviation, whether induced by drift, misconfiguration, or attack, can be swiftly detected and remediated.
OWASP AI Security and Privacy Guide
OWASP's AI Security and Privacy Guide adapts the well-known OWASP testing methodology to the unique needs of AI systems. It provides threat models, risk assessment templates, practical testing checklists, and mitigation strategies targeting common vulnerabilities. The guide covers everything from model input validation and prompt sanitization to post-deployment monitoring for drift and abuse.
Organizations implementing the OWASP Guide benefit from a community-driven, open-source baseline for AI security hygiene. Regularly updating security processes according to the guide’s evolving recommendations enables AI teams to harden models against new threats and ensure privacy protections are robust and verifiable.
Securing AI systems involves several complex and evolving challenges. These challenges arise from the unique properties of AI—such as its dependency on data, model opacity, and adaptability—and are compounded by the growing sophistication of threat actors. Below are some of the key difficulties in implementing effective AI security:
- Lack of standardized threat models: Unlike traditional IT systems, AI lacks well-defined, universally accepted threat models. Each AI application may face a unique set of vulnerabilities depending on its data, architecture, and deployment environment, making it hard to generalize defenses.
- Data dependency and exposure risks: AI models are only as secure as their training data. Inadequate data governance can lead to data poisoning, leakage of sensitive information, or biased outputs. Ensuring the provenance and integrity of training datasets is a persistent challenge.
- Model opacity and limited interpretability: Many AI models—especially deep learning systems—are opaque, making it difficult to understand their behavior or detect when they’ve been compromised. This limits the effectiveness of traditional monitoring and debugging tools.
- Adversarial robustness gaps: Defenses against adversarial inputs are often reactive and narrow in scope. Attackers continue to find new ways to craft adversarial examples that bypass detection, highlighting the gap between theoretical robustness and practical protection.
- Ecosystem complexity and supply chain risks: AI development relies heavily on third-party tools, open-source libraries, and external APIs. These dependencies introduce potential vulnerabilities that are often outside the direct control of the organization using the AI.
- Continuous learning and model drift: AI systems that learn or adapt in production can deviate from their original behavior over time. Monitoring for and responding to concept or data drift is technically challenging and resource-intensive.
- Evaluation and benchmarking limitations: Security tools and practices for AI are still emerging. There’s a lack of mature frameworks for testing AI-specific security properties, such as resilience to prompt injection or robustness under targeted perturbations.
- Integration with legacy systems: Integrating AI into existing IT infrastructure may expose new attack surfaces or weaken established security boundaries, especially when AI systems interact with less secure components.
- Regulatory uncertainty: The fast-evolving regulatory landscape for AI means organizations must anticipate compliance requirements that are still being formulated, increasing the complexity of risk management and long-term planning.
AI Firewalls
AI firewalls are purpose-built security layers positioned between AI models and their inputs, outputs, or API endpoints. They inspect, filter, and sanitize data to block malicious payloads such as adversarial examples, prompt injections, or untrusted API calls, before they reach production AI systems. Modern AI firewalls may leverage their own models to dynamically adapt filters and recognize emerging attack patterns.
Deployment of AI firewalls allows for centralized policy enforcement and threat intelligence sharing across distributed AI applications. As these defenses mature, they are increasingly integrated with organizational SIEM (Security Information and Event Management) and API management tools, enabling organization-wide visibility and response orchestration.
Runtime AI Monitoring and Response
Runtime AI monitoring and response systems focus on the in-production phase of AI operation. They continuously audit model activity, monitor input/output flows for integrity, and profile user interactions. The goal is to detect attacks such as adversarial exploitation, prompt abuse, or unauthorized model access, in real time, and trigger automated containment or remediation workflows.
Effective runtime monitoring bridges the gap between development-time assurance and operational security. Integrated response capabilities, including rollback, session isolation, or automated patch deployment, are essential for minimizing the impact of breaches and preserving trust in mission-critical AI applications.
AI Security Posture Management (AI-SPM)
AI Security Posture Management tools aggregate configuration, vulnerability, and compliance data across all AI assets—models, datasets, pipelines, and supporting infrastructure. These platforms provide a unified dashboard for tracking policy adherence, security control effectiveness, and supply chain dependencies. AI-SPM systems support automated compliance reporting, inventory management, and risk scoring, facilitating governance at scale.
With AI environments becoming more complex and interconnected, posture management tools are vital for holistic oversight. They enable security teams to proactively identify gaps, enforce safeguards, and plan remediation before risks are realized in production systems.
1. Implement Secure Development Lifecycles for AI
A secure development lifecycle (SDLC) tailored for AI systems embeds risk assessment, threat modeling, and security testing into every phase of model creation. This includes secure handling of datasets, regular vulnerability scanning of model code and dependencies, and tracking all changes in model weights or architecture. Integrating these practices with version control ensures traceability from development to deployment.
Effective AI SDLCs include both manual code reviews and automated security assessments. They require cross-functional collaboration between data scientists, engineers, and security teams to identify and mitigate risks early. Regular retrospectives and post-mortems on incidents within the SDLC drive continuous improvement, helping organizations adapt to new threats over time.
2. Establish Continuous AI Risk Assessment
Continuous risk assessment is necessary to keep pace with evolving threats and operational changes. This process involves regular evaluation of training data sources, assessment of model drift, and penetration testing targeting AI-specific vulnerabilities. Tools for automated risk scoring and inventory tracking help maintain visibility over all AI assets and dependencies.
Embedding risk assessment into deployment and change management workflows ensures that every model release or update is reviewed for emerging threats and compliance with security policies. Continuous risk management minimizes the potential for unmitigated risks to accumulate or go undetected in production.
3. Apply Model Access Controls and Encryption
Robust access controls restrict who can view, modify, or deploy AI models, and ensure that only trusted processes interact with sensitive components. This includes implementing multi-factor authentication, role-based permissions, and regular review of user privileges. Encryption must protect both model artifacts and any data in transit between training, storage, and deployment environments.
Automated auditing and logging of access and modification events help detect unauthorized changes or insider threats. Encryption strategies should align with regulatory mandates and industry standards, ensuring that even if a breach occurs, stolen models or datasets remain unusable to attackers.
4. Enforce Governance and Compliance Monitoring
Governance frameworks establish the rules, processes, and accountabilities for AI system security. These frameworks define requirements for change management, incident reporting, model validation, and ethical review. They also coordinate security efforts across distributed teams and technology stacks, reducing gaps and minimizing duplication.
Compliance monitoring tools automate the validation of policy adherence, providing ongoing oversight and eliminating reliance on ad hoc, manual checks. Real-time compliance tracking supports rapid remediation and improves confidence in the organization’s ability to meet internal and external obligations.
5. Build Multi-Layer AI Incident Response Plans
Incident response for AI systems requires plans that can address model-specific attack vectors, such as data poisoning or adversarial exploitation. These plans should include playbooks for rapid isolation or rollback of affected models, forensic review of training and inference logs, and clear roles for cross-disciplinary response teams. Simulated breach exercises, covering both technical and reputational risks, ensure readiness for real-world incidents.
A multi-layer approach to incident response coordinates actions across security, engineering, legal, and communications teams. Fast, coordinated responses minimize the damage caused by successful attacks and accelerate recovery. Continuous learning cycles—drawing on incident post-mortems and near-miss analyses—support iterative improvement in both technical defenses and team readiness.
As AI systems move from experimentation into production, their attack surface extends beyond models to include data pipelines, APIs, application logic, and automated decision workflows. Radware helps organizations secure these environments by enforcing strong controls around how AI systems are accessed, how they interact with other services, and how abuse or misuse is detected and contained. While Radware does not modify AI models themselves, its solutions play a critical role in protecting the surrounding infrastructure that determines whether AI-driven attacks succeed or fail.
Cloud Application Protection Service
AI systems are increasingly exposed through APIs and integrated into business-critical workflows. Radware’s Cloud Application Protection Service provides API security, behavioral anomaly detection, and automated enforcement that help prevent unauthorized access to AI services. By enforcing schema validation, rate controls, and least-privilege access, it ensures that AI-generated actions cannot exceed their intended scope, even if a model is manipulated or misused.
Cloud WAF Service
Many AI interactions occur through web applications, dashboards, and user-facing interfaces. Cloud WAF protects these entry points by filtering malicious inputs, sanitizing outputs, and blocking exploit techniques that can be used to influence or abuse AI-driven functionality. This helps prevent AI systems from being manipulated through compromised web forms, injected content, or insecure application logic.
Bot Manager
AI systems are frequent targets of automated abuse, including scraping, probing, and large-scale testing of attack techniques. Radware Bot Manager detects and mitigates non-human traffic attempting to exploit AI endpoints, protecting models and APIs from automated reconnaissance, misuse, and resource exhaustion. This is especially important for preventing large-scale experimentation with adversarial inputs.
Cloud Network Analytics
Effective AI security requires continuous monitoring of how systems behave in production. Cloud Network Analytics provides visibility into traffic patterns, API usage, and anomalous behavior across hybrid and multi-cloud environments. This allows security teams to identify unexpected activity linked to AI workflows, detect emerging threats early, and support ongoing AI risk assessment efforts.
Threat Intelligence Subscriptions
AI infrastructure is often targeted using known malicious networks and automation frameworks. Radware’s Threat Intelligence continuously updates protection systems with indicators of active attackers, enabling proactive blocking of suspicious sources before they interact with AI services. This supports governance and compliance efforts by reducing exposure to known high-risk entities.
DefensePro
Availability is a core pillar of AI security, particularly for AI systems embedded in operational workflows. DefensePro protects AI infrastructure from volumetric and protocol-level attacks that can disrupt access to models, APIs, or training environments. By maintaining service availability during attacks, organizations can preserve visibility and control when responding to AI-related security incidents.
Together, these solutions support a layered AI security strategy aligned with modern best practices. By securing the application interfaces, APIs, automation channels, and network paths that connect AI systems to the rest of the organization, Radware helps reduce risk, enforce governance, and strengthen resilience across the full AI lifecycle.