AI Application Security: Testing and Best Practices

AI is becoming deeply integrated into enterprise operations, powering everything from customer interactions to developer productivity. But as adoption accelerates, security remains a pressing concern. In fact, 72% of security leaders agree that the age of AI will need to reset how organizations look at application security.

Why? Because traditional AppSec tools weren’t built to secure AI models, data pipelines, or dynamic inputs and outputs. Organizations need new approaches, tailored testing, and AI-native platforms that can address these emerging risks. 

This guide examines the principles of AI application security and provides proven steps and best practices for deploying enterprise-grade solutions.

Key takeaways:

• AI applications introduce new attack surfaces. Beyond traditional flaws, risks include prompt injection, data poisoning, adversarial inputs, and compromised supply chain components like pre-trained models and datasets.
• Insecure AI creates business, regulatory, and reputational fallout. Data leaks, unsafe outputs, and compliance failures can trigger fines, disruption, and loss of customer trust.
• AI security demands lifecycle-wide, AI-native practices. Teams must embed DevSecOps into AI workflows with supply chain audits, model validation, targeted testing, hardened deployments, and continuous monitoring.
• Cycode provides the AI-native Application Security platform enterprises need. With scanners built for AI, automated remediation, CI/CD integration, and runtime monitoring, Cycode secures every layer of the AI lifecycle.

What Is an AI Application?

An AI application is software that integrates artificial intelligence to perform tasks traditionally requiring human intelligence, such as natural language processing, image recognition, decision-making, or prediction. Unlike traditional software, which follows predefined rules and logic, AI applications learn from data, adapt to new inputs, and improve over time.

Examples of AI applications include:

• Chatbots and virtual assistants 
• Recommendation engines 
• Fraud detection systems 
• Healthcare diagnostics tools 
• Agentic AI systems 
• Retrieval-augmented generation (RAG) apps 

As we’ve said, these applications are increasingly embedded in enterprise workflows, where they drive efficiency, automation, and innovation.

Why the Security of AI Applications Is Critical 

Imagine a customer service chatbot tricked into revealing sensitive account details, or a fraud detection system bypassed by a carefully crafted adversarial input. In the first case, attackers could gain direct access to personal financial information, exposing customers to fraud and the business to regulatory fines. In the second, criminals could slip fraudulent transactions past defenses, leading to millions in losses and shaken customer trust.

The bottom line: unsecured AI applications could expose you to risks like: 

• Data Breaches and Exposure: Compromised AI systems can leak sensitive training data, user information, or intellectual property, creating widespread regulatory, financial, and operational risk across the enterprise ecosystem.
• Model Misalignment and Unsafe Outputs: Poorly protected models can be manipulated to generate biased, harmful, or misleading results, undermining trust in AI systems and introducing legal and ethical consequences for organizations.
• Compliance Violations: Emerging regulations like the EU AI Act require strict oversight. Insecure applications risk failing audits, facing fines, and losing certifications necessary for operating in regulated industries.
• Reputational Damage: AI failures quickly make headlines. Security lapses that produce offensive, inaccurate, or manipulated outputs erode customer trust, investor confidence, and overall brand equity in highly competitive markets.

Adversarial Exploits in Production: Attackers use subtle manipulations to bypass AI defenses or force incorrect decisions. These exploits jeopardize safety, availability, and reliability in mission-critical enterprise systems and workflows.

AI App Vulnerabilities That Can Impact Your Enterprise

When people think about AI risks, they often focus on just one category (usually security flaws in the model). But vulnerabilities in AI applications span much more than that. They include direct security threats, safety issues in model behavior, and risks in the broader supply chain. 

Security Threats

AI applications introduce novel security vulnerabilities that adversaries actively exploit. Unlike traditional exploits, these target the model itself or its surrounding systems, creating unique exposure pathways. Examples include:

• Prompt injection and jailbreaks that override guardrails.
• Data poisoning during training to skew outputs.
• Adversarial inputs designed to cause misclassification or bypass detection.
• Model theft exposing intellectual property or enabling downstream attacks.

Safety Concerns

Beyond direct breaches, AI systems pose safety risks when manipulated or misaligned. These issues undermine reliability and trustworthiness rather than infrastructure. Key concerns include:

• Hallucinations, where models generate convincing but false information.
• Bias amplification, reinforcing harmful stereotypes in sensitive contexts.
• Toxic outputs, such as offensive or unsafe responses in customer-facing apps.
• Overconfidence, where models deliver incorrect answers with unjustified certainty.

Supply Chain Risks

AI applications depend on vast ecosystems of models, datasets, and open-source libraries — each a potential entry point for compromise. Attackers can exploit:

• Pre-trained models embedded with hidden backdoors.
• Corrupted training datasets introducing subtle vulnerabilities.
• Insecure third-party APIs feeding malicious inputs or exfiltrating data.
• Open-source dependencies that lack proper vetting or version control, propagating risk across environments.

Addressing all three is critical, and responsibility often spans multiple teams, from security and compliance to engineering, DevOps, and data science.

How Security for AI Differs from Traditional AppSec

Traditional applications are deterministic. That means, given the same input, they’ll always produce the same output. AI applications, on the other hand, are probabilistic and adaptive, which makes their behavior harder to predict and secure. This fundamental difference means common vulnerability categories look very different in AI systems compared to traditional software.

Let’s take a closer look:

The Role of DevSecOps in AI Model Security

Remember: AI application security doesn’t sit with one team alone. Data scientists, developers, compliance officers, and security engineers all play a part. 

That’s why DevSecOps principles are essential. They ensure security is embedded throughout the AI development lifecycle rather than bolted on at the end. By aligning frameworks, processes, and people, organizations can build security into every stage.

Key DevSecOps practices for AI applications include:

• Secure Model Training: Ensure datasets are clean, validated, and access-controlled, preventing data poisoning and unauthorized tampering. Training environments should follow least-privilege principles and strong governance controls.
• Pipeline-Level Testing: Integrate scanning for code, dependencies, and models directly into CI/CD pipelines. Automating checks reduces bottlenecks while keeping vulnerabilities from slipping into production.
• Production-Level Observability: Monitor deployed models for drift, decay, or performance anomalies. Real-time visibility helps teams catch subtle failures or signs of adversarial manipulation before they escalate.
• Feedback Loop Integration: Establish feedback channels between security, engineering, and data science teams. Insights from runtime incidents should flow back into retraining, patching, and updating processes to improve resilience.

Runtime Threat Detection: Continuously monitor AI applications for adversarial inputs, prompt injection attempts, or abnormal behavior. Embedding detection at runtime provides the last line of defense against evolving threats.

Regulatory Frameworks Impacting AI Security

AI governance is evolving rapidly, and organizations can’t afford to treat compliance as an afterthought. From international standards bodies to regional lawmakers, new rules are shaping how enterprises must secure AI applications. Here’s what you should know: Global AI Security Standards

Global AI Security Standards

• NIST AI Risk Management Framework (RMF): Provides a structured approach for governing, mapping, measuring, and managing AI risks. Widely referenced in the U.S. and beyond, it helps organizations embed accountability and transparency across AI development and deployment.
• ISO/IEC 23894: Offers guidance on integrating AI risk management into existing organizational processes. It emphasizes lifecycle management, risk assessment, and alignment with broader information security standards.
• ISO/IEC 42001: The first AI management system standard (AIMS), designed to help organizations establish governance, security, and compliance processes for AI. It sets requirements for policies, training, monitoring, and continuous improvement.
• OWASP Top 10 for LLM/GenAI: A community-driven list of the most critical risks facing AI and LLM applications. It provides developers and security teams with practical mitigation strategies for issues like prompt injection and insecure output handling.

Regional and Country-Level Regulations

• EU AI Act: The world’s first comprehensive AI law, establishing risk-based obligations for providers and deployers. Requirements for general-purpose AI begin in 2025, with broader compliance deadlines extending into 2026–2027. It introduces strict rules for transparency, documentation, and post-market monitoring.
• United Kingdom AI Regulation: The UK is creating the AI Security Institute to test AI systems and evaluate emerging risks. While less prescriptive than the EU Act, the UK framework prioritizes safety testing, resilience, and alignment with global best practices.
• United States Guidance: Federal agencies continue to rely on the NIST AI RMF after the repeal of Executive Order 14110. State regulators and financial authorities are publishing AI-specific cybersecurity guidance, particularly around data privacy, algorithmic accountability, and risk governance.

Sector-Specific Requirements

• Healthcare: The U.S. Food and Drug Administration has released draft guidance on lifecycle oversight of AI-enabled medical devices. Combined with HIPAA updates, healthcare organizations are expected to implement rigorous risk analysis, monitoring, and documentation for AI used in patient care.
• Financial Services: Regulators emphasize model risk management and resilience for AI used in trading, credit scoring, and fraud detection. Agencies like the Treasury and OCC highlight the importance of governance frameworks to ensure responsible AI adoption in high-stakes environments.
• Payments (PCI DSS 4.0): The updated Payment Card Industry Data Security Standard, effective in 2024, now requires stronger encryption, monitoring, and testing. Guidance released in 2025 underscores the need to evaluate AI-driven payment tools for compliance with PCI standards.

Want to stay up-to-date? Authoritative sources include the EU’s AI Office updates, NIST’s AI RMF, ISO standards pages, and OWASP’s GenAI project.

Steps to Securing AI Applications for Deployment

Understanding the risks is only the first step. To truly safeguard AI applications, security leaders need a practical framework for preparing systems before they go live. Below are five essential stages every enterprise should follow to ensure AI applications are secure, compliant, and production-ready.

1. Audit the AI Supply Chain

Before deployment, map and verify all dependencies that feed into your AI system. This includes pre-trained models, open-source libraries, datasets, and third-party APIs. You should:

• Verify integrity and provenance of external components.
• Scan open-source dependencies for vulnerabilities or hidden backdoors.
• Establish version control and monitoring for all critical assets.

2. Validate Model Behavior

Don’t assume models perform as intended. Validate functionality against both expected use cases and potential misuse scenarios. You should:

• Run tests to confirm outputs align with business goals.
• Evaluate for hallucinations, bias, or misalignment.
• Conduct “red team” exercises to identify failure modes under adversarial inputs.

3. Test for Security and Safety

Go beyond functional validation with targeted testing that simulates real-world threats, for example:

• Apply traditional security tests (SAST, DAST, dependency scanning).
• Use AI-specific testing, such as prompt fuzzing or adversarial input injection.
• Document vulnerabilities and ensure fixes are prioritized before production rollout.

4. Implement Runtime Protections

Security doesn’t end at deployment. Runtime guardrails help detect and block issues as they emerge. Be sure to:

• Apply access controls and API security policies.
• Add input/output filters to sanitize prompts and responses.
• Integrate anomaly detection systems to flag suspicious interactions in real time.

5. Monitor and Log Continuously

AI applications evolve with their data — meaning yesterday’s secure model can become tomorrow’s risk. To future-proof your AI ecosystem:

• Track performance, drift, and decay across deployed models.
• Log model decisions and system activity for auditing.
• Feed runtime insights back into training and testing cycles to improve resilience.

Choosing the Best AI Application Vulnerability Scanning Tools

Traditional AppSec scanners aren’t enough for AI. Models behave differently than deterministic applications, and detecting risks requires specialized tooling. Choosing the right scanner means balancing coverage, accuracy, and compliance while ensuring it actually works for your architecture. 

Here’s how to evaluate vendors:

Model Compatibility

Your scanning tool must align with the types of AI systems you deploy, whether they’re LLMs, ML models, or multi-modal pipelines. Without compatibility, coverage gaps will persist. 

Consider the following:

• Does the scanner support both proprietary and open-source models?
• Can it handle model updates and fine-tuning?
• Is it optimized for your runtime environment (cloud, on-prem, hybrid)?

Security and Safety Coverage

AI risks go beyond SQL injection or misconfigurations—bias, unsafe outputs, and adversarial prompts need testing too. Coverage should span both security and safety vulnerabilities.

Consider the following:

• Does it test for prompt injection and data exfiltration?
• Can it identify misaligned or harmful outputs?
• Does it cover model supply chain vulnerabilities (e.g., poisoned training data)?

Black Box and API Testing Support

AI applications often expose APIs, making them prime targets. Effective tools must test both at the API level and in black-box conditions.

Consider the following:

• Does it simulate real-world attack patterns against APIs?
• Can it scan without requiring full model access?
• Does it integrate into existing API security workflows?

Threat Intelligence Integration

Modern threats evolve too quickly for static scanning alone. Integration with live threat intelligence ensures detection keeps pace with new attack techniques.

Consider the following:

• Does the tool integrate with threat feeds relevant to AI?
• How often are detection signatures updated?
• Does it leverage community or industry threat-sharing programs?

Reporting and Compliance Features

Security teams need more than raw findings. They need evidence for audits, prioritization, and regulatory compliance. Reporting should be actionable and aligned to frameworks.

Consider the following:

• Does the tool map findings to AI-specific standards?
• Are reports customizable for developers, executives, and auditors?
• Can it auto-generate compliance evidence?

AI Security Best Practices 

Working with customers deploying AI at scale has shown us a clear truth: securing AI requires both traditional AppSec discipline and AI-native techniques. And just as attackers use AI to evolve, defenders must use AI to strengthen resilience. 

Here are practices that consistently move the needle:

• Shift Left on Security: “Shift left” is a mantra across AppSec for good reason—it reduces cost and speeds fixes by catching issues earlier. In AI, it means testing datasets, pipelines, and models from the earliest development phases rather than waiting for production deployment.
• Validate Model Inputs and Outputs: AI can be exploited at both ends, through manipulated inputs or dangerous outputs. Teams must validate training data for poisoning, filter user inputs for prompt injection, and review generated outputs for unsafe, biased, or noncompliant responses before they impact customers.
• Monitor for Emergent Threats: AI models evolve with use, meaning new risks can surface after deployment. Continuous monitoring helps detect drift, adversarial behavior, or novel attack patterns. Proactive observability ensures you catch changes before they cascade into system-wide vulnerabilities.
• Enforce Secure Model Deployment: Models should only move to production through hardened, auditable pipelines. This includes enforcing access controls, securing environments, and ensuring version tracking. A disciplined deployment process helps prevent tampering and ensures models behave consistently across environments.

Automate Continuous Scanning: AI application security cannot be a one-time checkpoint. Automating vulnerability scanning across code, APIs, and models ensures ongoing protection. Look for tools that integrate seamlessly into CI/CD pipelines to reduce manual overhead while maintaining high coverage.

Ensure Security in AI Development With Cycode

Securing AI applications requires more than retrofitting traditional AppSec tools. It demands platforms designed with AI in mind. 

Cycode is the leading AI-native Application Security platform, empowering enterprises to secure every stage of the AI development lifecycle, eliminating gaps between teams, processes, and environments.

Key capabilities include:

• Proprietary scanners and integrations purpose-built for AI workloads, covering code, models, data pipelines, and third-party tools.
• AI-driven automation that detects and remediates vulnerabilities faster while reducing manual overhead.
• Policy enforcement to ensure compliance across training data, deployment environments, and runtime operations.
• Continuous testing and scanning that shift security left and extend protection into production.
• Seamless DevSecOps integration with CI/CD pipelines for frictionless adoption by engineering teams.
• Advanced observability and monitoring to detect drift, adversarial activity, or emergent threats in real time.

Book a demo today and discover how Cycode can enhance AI application security for your enterprise.