Best practices for secure AI development in 2025

Artificial intelligence is changing how organizations operate and make decisions. AI systems are now used to process sensitive data, make predictions, and automate tasks across many industries. Building these systems securely is different from securing traditional software.

Security and compliance in AI development have become important topics. Many people are searching for best practices that address the risks unique to AI. This article explains the most current approaches and frameworks for secure and compliant AI development.

Why Secure AI development demands new thinking

AI systems bring security risks that differ from traditional software applications. These risks often come from how AI models are trained, the data they use, and how they interact with users and other systems.

Traditional software security focuses on protecting code and data from unauthorized access or modification. In AI, the model itself can become a target. Unique vulnerabilities include:

• Data poisoning: Attackers introduce harmful data during training to influence model outputs
• Model theft: Someone copies or extracts the trained model, gaining access to its intellectual property and possibly its training data
• Prompt injection: An attacker manipulates the input to an AI system to produce unintended or unsafe outputs

Because AI systems learn from data and user interactions, they can be exposed to new and unpredictable threats. Traditional cybersecurity frameworks often do not address these AI-specific risks.

Current Regulatory Landscape for AI Compliance

AI development is affected by regulations that are specific to how data is collected, used, and shared. Different laws and standards apply depending on where an organization operates and what kind of data is involved.

GDPR and CCPA Obligations

The General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States regulate how personal data is used in AI systems. AI models that use personal data for training or user interaction follow data protection rules.

GDPR includes requirements such as data minimization, which means collecting only the data needed for the intended purpose. It also establishes the right to explanation, which gives individuals the ability to know how automated decisions affecting them are made.

HIPAA and Industry-Specific Mandates

In healthcare, the Health Insurance Portability and Accountability Act (HIPAA) sets rules for how medical data can be used in AI applications. Financial services follow rules like the Gramm-Leach-Bliley Act, which covers the security and confidentiality of personal financial information.

EU AI Act

The European Union Artificial Intelligence Act (EU AI Act) is the world’s first comprehensive AI regulation, formally adopted in 2024. The law classifies AI systems into unacceptable risk, high risk, limited risk, and minimal risk categories, with specific compliance obligations for each.

• High-risk AI systems (such as those used in healthcare, financial services, or critical infrastructure) must implement strict documentation, transparency, human oversight, and security controls.

• Implementation timeline: Most provisions take effect mid-2026 for high-risk systems, with earlier deadlines in 2025 for banned practices like social scoring.

• Global impact: Even non-EU companies must comply if they provide AI products or services affecting EU users.

Organizations operating in multiple regions should also review equivalent frameworks like Brazil’s LGPD, Singapore’s PDPA, and Canada’s PIPEDA to ensure global compliance alignment.

NIST AI Risk Management Framework(AI RMF)

The NIST AI Risk Management Framework (AI RMF), released in January 2023 and updated with Playbooks and Companion Guidance in 2024–2025, provides a voluntary but highly influential approach to building trustworthy AI systems.

The framework focuses on:

• Map – Contextualizing AI risks

• Measure – Assessing and analyzing risks using defined metrics

• Manage – Prioritizing and mitigating risks

• Govern – Embedding governance and accountability practices throughout the AI lifecycle

The latest NIST updates include:

• Detailed guidance for Large Language Models (LLMs) and generative AI

• Alignment with the ISO/IEC 42001 AI Management System Standard

• Sector-specific risk templates for healthcare, finance, and critical infrastructure

• Recommendations for continuous monitoring and AI incident reporting

While AI RMF is US-based, its adoption is growing internationally, especially among multinational companies seeking a globally recognized AI governance model.

Core principles of secure-by-design AI architecture

Secure-by-design in AI means planning for security from the very beginning of building an AI system. This approach helps address unique risks that AI systems face, rather than adding security steps at the end.

Least privilege and zero trust for AI pipelines

The principle of least privilege limits each person or process to only the resources and data required for their role. Zero trust means no person or system is automatically trusted, even if inside the organization.

Examples of least privilege and zero trust in AI pipelines:

• Data scientists receive read-only access to production data, but no ability to change model code in production
• Separate service accounts for each step in the AI workflow control access to data, training environments, and model repositories
• Multifactor authentication for anyone accessing sensitive AI training data or deployment systems

Defense in depth across model layers

Defense in depth uses multiple layers of security controls throughout the AI system. These layers protect against different types of threats at each stage, from data collection to model outputs.

Examples of protective measures:

• Validating and cleaning input data before it is used for training or inference
• Encrypting data at rest and in transit between systems and environments
• Filtering outputs from AI models to block or flag harmful or sensitive content
• Logging and monitoring all model interactions and decisions for security review

Step-by-Step AI security assessment

An AI security posture assessment is a way to check how safe and secure an AI system is. This process can help organizations find weaknesses, understand risks, and make better decisions about protecting their AI systems.

Inventory Data, Models, and Dependencies

Start by listing all data, AI models, and related components used in the system. Include where data comes from, how it is stored, and what types of AI models are in use. List third-party tools, integrations, and open-source libraries, since these may introduce additional risks or dependencies.

Classify and map regulatory requirements

Review the types of data handled by the AI system and identify which laws or regulations apply. For example, medical data may be subject to HIPAA, while data from European users may be covered by GDPR. Create a map that shows which regulations are linked to each data type, model, or use case.

Identify threats and attack surfaces

Look for possible ways attackers could harm the AI system or misuse its data. Common AI-specific threats include:

• Data poisoning: Inserting incorrect or malicious data into training datasets to change model behavior
• Model inversion: Using model outputs to reconstruct sensitive training data
• Adversarial examples: Submitting specially crafted inputs that cause the model to make mistakes
• Supply chain attacks: Compromising third-party tools, libraries, or pre-trained models before integration

Prioritize and remediate gaps

After finding risks and weaknesses, rank them based on how much harm they could cause and how likely they are to happen. Focus first on the vulnerabilities that could have the greatest impact or are easiest for attackers to exploit.

Best practices for protecting data across the AI lifecycle

Data used in artificial intelligence projects passes through several stages, including collection, storage, model training, deployment, and user interaction. At each stage, different risks can appear, so data security controls are applied throughout the entire lifecycle.

Data Security Posture Management

Data Security Posture Management (DSPM) is a method for monitoring and understanding the security status of data in AI systems. DSPM tools continuously track where sensitive data is stored, how it moves, and who accesses it. Data is regularly classified based on its sensitivity, such as public, internal, confidential, or regulated.

Encryption and Tokenization

Encryption converts data into a coded format, which can be changed back only with a special key. Data is often encrypted when stored (at rest) and when sent between systems (in transit). Tokenization replaces sensitive data elements with random tokens. The original data is stored in a secure location.

Encryption is used when the data must be decrypted and used by the AI system, while tokenization is used when the data does not need to be visible in its original form during processing.

Role and Attribute based access controls

Role-based access control (RBAC) restricts what data and systems each person can access based on their job role. For example, a data scientist might have access to anonymized data for training models, while an engineer might have permissions to deploy models but not view training data.

Attribute-based access control (ABAC) uses attributes like location, device type, or project to decide access. For instance, a user can only access certain data if they are working on a specific project or using an approved device.

Building robust and tamper-resistant models

AI models face unique security challenges. Attackers sometimes try to trick these systems or make them behave in ways that were not intended. This is called an adversarial attack. In adversarial attacks, someone changes the data sent to the model or the prompts given to it, causing the model to make mistakes or give out information it was not supposed to share.

Adversarial training techniques

Adversarial training is a process that helps a model learn to recognize and ignore trick inputs. During training, the system is shown examples of normal data and also data that has been purposely changed in small, hard-to-detect ways. The model learns to spot these changes and respond correctly.

Red-Team and Penetration testing for LLMs

Red-teaming is a type of testing where people act like attackers to find weaknesses in the system. In AI, red teams often try to find ways to break the model or make it behave in unexpected ways. One example is prompt injection, where someone gives the model a carefully written prompt that causes it to say or do something it usually would not.

Output filtering and prompt injection guards

Output filtering and prompt injection guards are tools and rules that check what goes into and comes out of an AI model. They work in real time to stop harmful or sensitive information from being shared.

Some filtering techniques include:

• Checking prompts for unsafe or suspicious words before sending them to the model
• Scanning the model’s answers for private or restricted information before sharing them with users
• Using lists of blocked words or phrases to catch common attack attempts
• Applying pattern recognition to spot prompts that look like known attacks

Continuous monitoring and incident response for AI systems

Ongoing security oversight for AI systems uses both automated tools and human review. AI models in production can change how they behave over time, sometimes in ways that are not expected.

Real-time drift and anomaly detection

Model drift occurs when an AI system’s predictions or outputs start to change over time, often because the data it receives is different from the data it was trained on. Drift can cause the model to make mistakes or behave unpredictably.

Anomaly detection tools look for outputs or behaviors that are not normal. These tools compare current AI activity with what is expected. If the system starts making unusual predictions or responses, the monitoring tools flag these events for review.

Automated policy enforcement and guardrails

Automated policy enforcement uses rules and controls that are built into the AI system. Guardrails help prevent the system from breaking rules or producing unsafe results.

Common guardrail types include:

• Input validation rules that check if the data sent to the model is safe and allowed
• Output filters that block or modify responses containing restricted or sensitive information
• Rate limits that control how often users can interact with the system
• Access controls that restrict who can use specific AI features or data

Governance, Auditability, and AI SBOM documentation

Oversight for artificial intelligence systems involves clear documentation and tracking of how models are created, changed, and used. Governance covers who is responsible for each part of the AI system and how decisions are made regarding updates or changes.

Versioning and change management workflows

Versioning in AI tracks every change to a model, dataset, or codebase. Each time a model is updated, a new version is created. Change management workflows record who approved a change, what was changed, and when the change happened.

Traceability from data source to prediction

Traceability means keeping a record of every step from collecting data to making a prediction or decision with an AI model. This record includes information about where data was collected, how it was prepared, which model was used, and how the output was generated.

Software bill of materials for AI components

A Software Bill of Materials (SBOM) for AI components is a detailed list of all software parts, libraries, and dependencies that make up an AI system. Each item in the SBOM includes information about its version, source, and security status.

The SBOM helps teams identify if a vulnerability is present in any part of the system. By tracking all dependencies, organizations can respond quickly when a security problem is found in a library or tool used by their AI models.