AI Security: Risks, Frameworks & Best Practice

As AI becomes a standard component of enterprise software — whether as standalone tools, integrated into larger systems, or deployed as private AI (models and infrastructure managed within an organization’s secure environment) — organizations must update their cybersecurity strategies to keep pace with this evolving technology.

AI is also increasingly embedded into the software development lifecycle (SDLC), offering new efficiencies but also exposing novel points of vulnerability. If security risks specific to AI are not addressed from planning through deployment, enterprises may face large-scale or entirely new forms of cyber attack.

For industries such as finance, healthcare, and retail, where trust and data integrity are crucial, AI security is no longer optional; it’s foundational.

In this article, we’ll explore AI security—what it is, how secure today’s AI systems really are, the key risks enterprises face, and the frameworks and best practices that can help mitigate them.

What is AI security?

AI security refers to the policies, processes, and protective measures designed specifically to safeguard AI systems, including the models (algorithms that learn from data), datasets (the sensitive information these models process), and supporting infrastructure (such as deployment pipelines or monitoring tools).

For enterprises using private or customized AI solutions, particularly in finance, healthcare, or retail, protecting proprietary or sensitive data processed by AI systems is a critical responsibility.

As AI becomes embedded in essential functions like fraud detection, patient diagnostics, or inventory forecasting, it also expands the potential attack surface. Without proper safeguards, integrated AI systems can create new vectors for cyber threats.

How secure is artificial intelligence?

Due to the complexity of AI models and their supporting infrastructure, organizations must address a range of security risks across the entire architecture. 

AI Adoption may also expose gaps in existing cybersecurity measures, especially during integration and deployment, where legacy systems may be less prepared to handle the specific needs of AI.

As with other complex systems, particularly when utilizing public or third-party models, AI implementations necessitate customized configurations to align with enterprise security protocols. This may include adding protective layers, such as a zero-trust framework (a model that assumes no internal or external actor is inherently trusted), applying encryption, or ensuring regulatory compliance.

These measures are particularly crucial in regulated industries, such as financial services and healthcare, where data privacy and auditability are of paramount importance. 

AI is not inherently insecure, but it does demand deliberate governance and human oversight to remain protected against evolving threats.

What are the key AI security risks?

AI models have become a frequent target for attackers, introducing new and unique security vulnerabilities, including at the external perimeter and within the models themselves. As these systems are increasingly integrated into enterprise software and the software development lifecycle (SDLC), they expand the surface area for potential breaches.

Prompt injections

These attacks manipulate AI behavior through maliciously crafted inputs — called prompts, or the text instructions an AI model responds to. In direct prompt injections, adversaries may extract confidential data or override intended responses. Indirect prompt injections involve guiding the AI to ingest untrusted external data, potentially allowing malware to propagate or misinformation to spread.

For example, in finance, an attacker might use prompt injection to access sensitive transaction summaries in a customer service chatbot.

Infected code and data

Attackers may compromise training inputs through data poisoning, a technique that involves inserting malicious or misleading information into the dataset. This degrades model reliability and may cause downstream application failures. Even subtle changes can introduce vulnerabilities or lead to incorrect predictions, known as evasion.

Enterprises using private or on-premises AI solutions generally face reduced risk here due to tighter control of training data.

Data integrity

Preserving data integrity is crucial in preventing privilege escalation and data exfiltration. If internal data or credentials are exposed or tampered with, attackers may use them to access systems beyond the AI application.

Hallucination abuse

AI models occasionally generate convincing but inaccurate responses — a behavior known as hallucination. In a hallucination abuse attack, adversaries exploit this flaw by triggering the model repeatedly until false information is treated as truth. This can lead to reputational damage, user misinformation, or vulnerabilities in customer-facing tools.

Key AI security frameworks and standards

Several security frameworks and standards, developed by leading industry bodies, help enterprises manage the risks introduced by AI systems. These resources offer guidance on vulnerabilities, governance, compliance, and best practices across the AI lifecycle. 

OWASP Top 10 LLM Security Risks

The Open Worldwide Application Security Project (OWASP) is a global foundation focused on improving software security. Its Top 10 lists highlight the most critical vulnerabilities identified by security professionals.

The LLM Security Top 10 outlines common threats associated with large language models (LLMs) and generative AI, including prompt injection, data poisoning, and the disclosure of sensitive information. These insights are particularly valuable for enterprises that deploy AI in public-facing applications, such as chatbots in retail or virtual assistants in financial services.

Google’s Secure AI Framework (SAIF)

SAIF presents six core principles that address AI security across the full lifecycle — from development to deployment. It emphasizes alignment between AI implementation and broader organizational security practices. This approach is particularly useful for enterprises scaling AI across departments while maintaining unified controls and policies.

NIST’s Artificial Intelligence Risk Management Framework (AI RMF)

Developed by the U.S. National Institute of Standards and Technology, this framework promotes trustworthy AI by guiding organizations through the design, development, deployment, and evaluation of AI systems.

For regulated enterprises, especially in healthcare and financial services, the NIST RMF can help satisfy regulatory expectations around transparency, accountability, and resilience.

Framework for AI Cybersecurity Practices (FAICP) – ENISA

Created by the European Union Agency for Cybersecurity, FAICP focuses on best practices across three layers:

• Cybersecurity foundations: governance, infrastructure, and compliance
• AI-specific risks: threat modeling, ethics, and trustworthy AI
• Industry-specific guidance: tailored to sectors including finance, healthcare, and retail, making this a particularly actionable resource for regulated enterprises.

MITRE’s Sensible Regulatory Framework for AI Security

MITRE’s Sensible Regulatory Framework for AI Security offers practical, regulation-aware guidance for securing AI systems without stifling innovation. It is best used in conjunction with MITRE’s ATLAS matrix.

MITRE ATLAS Matrix

The ATLAS matrix helps organizations assess AI systems across security, privacy, and compliance dimensions. For enterprise leaders, it supports alignment with both internal policies and external regulatory frameworks, making it a valuable tool for risk management and strategic decision-making.

How can enterprises ensure AI is developed securely?

Enterprises can strengthen AI security by embedding protective measures across the full development lifecycle, rather than addressing risks post-deployment. Secure AI development practices help ensure long-term resilience, regulatory alignment, and trust across stakeholders.

Risk assessments

Before initiating AI development or deployment, organizations should conduct a formal risk assessment to ensure a comprehensive understanding of potential risks and liabilities. Leverage the frameworks discussed earlier (such as NIST or MITRE) to guide the evaluation of risks across the AI lifecycle. This may include vulnerabilities related to third-party data sources, integration points, or regulatory exposure.

In healthcare, for example, assessments can help identify how an AI system may intersect with protected health information (PHI) and compliance mandates, such as the Health Insurance Portability and Accountability Act (HIPAA).

Security by design

Adopting a “security by design” approach means incorporating security principles into the model architecture, workflows, and organizational policies from the outset. Rather than bolting on security tools later, this approach ensures alignment with enterprise infrastructure and risk strategies.

For example, a retail enterprise deploying AI-powered product recommendation systems can integrate security policies directly into the API infrastructure to prevent injection risks or unauthorized data exposure.

Controls

Implementing robust access and data controls is essential. Enterprises should establish AI-specific policies that define who can access models, what data can be used, and under what conditions these models can be utilized.

Apply role-based access controls (RBAC) and maintain activity logs to track usage patterns. Use the principle of least privilege to ensure individuals can only access the systems necessary for their responsibilities — a requirement particularly relevant in financial institutions, where internal access control is closely audited.

Regular testing

Testing ensures the AI system functions correctly and securely throughout its lifecycle. This includes not only functional tests, but also security-focused evaluations. Integration testing helps surface hidden vulnerabilities as models connect with internal systems.

Test the training data for signs of manipulation or data poisoning, where malicious changes can lead to misbehavior. Standard testing approaches include:

• Unit testing (individual components)
• Integration testing (system-wide interaction)
• Penetration testing (simulated cyberattacks)
• Adversarial testing or red teaming (intentionally attempting to mislead or trick the model to uncover weaknesses)

Regular testing ensures AI behavior remains consistent, predictable, and aligned with the organization’s security posture.

Strengthening AI security through proactive development and governance

AI security is both challenging and complex, given the evolving threat landscape and the rapid pace of technological advancement. While AI systems are not inherently secure, there are critical steps organizations can take to significantly reduce risk and enhance protection.

By integrating security considerations throughout the entire AI development lifecycle and leveraging established industry frameworks, enterprises can proactively mitigate vulnerabilities and strengthen the security posture of their AI deployments.