What is AI Security? How to Secure Your AI Infrastructure from Cyber Attacks in 2025?

Did you know that the AI market value is projected to be over $2500 billion by 2032, with a growth rate of 19% per year? The modern, sophisticated world pairs firmly with artificial intelligence to do almost every other task. From academics to healthcare to the ecommerce sector, artificial intelligence has expanded its use in nearly every industry.

In a time where the world relies so much on AI, the question of its security and compliance comes into the picture. AI security undoubtedly becomes a genuine concern for several obvious reasons. In fact, as of 2025, almost 42 percent of fraudulent activities are AI-driven, showing a significant increase compared to the previous years.

With the widespread use of AI in our day-to-day lives, how do we secure it? What is AI security, and how does it mitigate common AI risks? This comprehensive guide on AI security will talk about everything you need to know about AI security. Let's get started!

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What Is AI Security?

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An acronym for artificial intelligence security, AI security simply refers to protecting AI systems & models from cybersecurity threats. In other words, AI security includes various measures and technologies designed to safeguard AI & LLM systems from unauthorized access, manipulation, and malicious attacks.

AI security involves many practices, from threat detection and securing data pipelines to predicting potential threats. But why is security in AI so essential?

Undoubtedly, AI has been a helping hand to the human workforce in their workplaces, making complex tasks easier. The McKinsey report also states a 250% rise in AI adoption from 2017 to 2022 across all industries and verticals. Organizations and businesses are using AI & LLM models for automating routine tasks but also for improving operational efficiency & decision-making.

However, despite its advantages, developing and deploying an AI model for public access & use requires robust fortifications. Furthermore, the risks and vulnerabilities associated with AI systems are also rising. Thus, with the increasing reliance on AI systems, the focus on their security is also rising.

Additionally, AI security is a multidisciplinary field that demands harmony among experts in machine learning, cybersecurity, software engineering, ethics, and various application domains. Using technical expertise and general precautions makes integrating AI security easier, ultimately ensuring privacy protection. On average, organizations availing AI security save USD 1.76 million on the costs of responding to data breaches.

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7 Emerging Security Threats Associated With AI Systems

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AI & LLM models comprise algorithms and vast datasets, which makes them highly complex and powerful. Furthermore, they also process sensitive user information, which makes them vulnerable to risks. Let's discuss below some of the emerging security threats associated with AI systems:

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1. Data Breaches

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AI systems often store a massive amount of user data; therefore, the possibility of data breaches is always on the line. Unauthorized access to confidential data is possible if an AI system’s data storage or transmission channels are compromised.

For instance, in 2018, hackers targeted and leaked over 3.75 million users' records from TaskRabbit. The incident temporarily shut down the company's website and app.

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2. Model Theft

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Commonly known as model inversion or extraction, model theft refers to the act of unauthorized copying or stealing of a machine learning model by a malicious actor. Doing so is possible when the malicious user somehow makes its way to the model’s architecture, parameters, and training data without consent, generally by exploiting vulnerabilities in the system or through reverse-engineering techniques.

Model theft has diverse consequences, including data privacy risks and loss of competitive advantage for the organization. One instance of Model Theft is the 2023 Meta LLaMA leak, where someone with authorized access shared Meta's large language model on a public internet forum.

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3. Resource Exhaustion Attacks

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Short for Resource Exhaustion Attacks, REAs occur when someone attempts to overwhelm AI systems by consuming their computational resources. Such an attack frames an AI system as incapable of functioning properly.

These attacks affect the technical functionality of an AI system, leading to deteriorated performance and denial of expected services. A real-time example is the 2018 Github DDoS attack, where attackers overwhelmed the AI system by exploiting Memcached, a database caching system, to amplify traffic.

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4. Bias and Discrimination

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The presence of any kind of biases in the training data can force the AI model to perpetuate or even amplify them. This can lead to discriminatory outcomes, especially in fields that demand fair judgment, such as hiring, lending, and law enforcement.

Such biased inclination from AI often causes ethical or legal issues, reducing users' trust in it. Amazon's 2014 hiring and screening bias is a prominent example of AI bias, wherein its AI-powered recruitment tool favored male candidates over female candidates by automatically rejecting their CVs.

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5. Adversarial Attacks

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Meticulously crafted malicious input data can easily convince AI to provide incorrect or false outcomes. Adversarial attacks target the loopholes in the AI system’s design via specific inputs, thus manipulating or deceiving it to deliver harmful outputs.

Some adversarial attacks include evasion attacks, poisoning attacks, and model inversion attacks, each aiming to accomplish a different purpose.

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6. Prompt Injection

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This security threat is usually associated with companies that dominantly use large language models (LLMs). The attack involves manipulating AI systems by injecting malicious content into prompts to produce unintended responses or actions.

Some of its consequences include bypassing safety mechanisms, leaking sensitive information, turning on malicious behavior, etc. For instance, in 2023, a user intentionally used prompt injection against the Bing AI chatbot, causing it to divulge its codename for debugging purposes.

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7. AI Hallucinations

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Another emerging security threat to AI systems is AI hallucination. It involves instances where AI-driven models produce responses that are not based on accurate data or facts. This kind of AI behavior often leads to inaccurate and misleading information that is harmful across various domains.

For example, Google's Bard chatbot once claimed that the James Webb Space Telescope had captured the world's first images of a planet outside our solar system. The incident raised concerns over how far trustworthy AI is.

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5 Benefits of a Secure AI Infrastructure

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According to Forbes, AI-related incidents have surged by 690% between 2017 and 2023—a staggering increase highlighting the growing risks associated with AI adoption. This sharp rise underscores the urgent need for businesses and organizations to prioritize building a secure, ethical, and resilient AI infrastructure. Below are some key benefits of a secure AI infrastructure.

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1. Defense Against Cyber Threats

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A secure AI infrastructure is less vulnerable to these risks because AI models are the primary targets of malicious actors and cybercriminals. Furthermore, it also has AI-specific threat detection, firewalls, guardrails and other such monitoring measures and systems to protect the model from such malicious attacks.

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2. Security, Reliability & Trustworthiness

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A secure AI infrastructure is safe and reliable and safeguards vast amounts of data being processed. It ensures compliance with global frameworks like GDPR, NIST, EU AI, CCPA, HIPAA, etc.

Additionally, it has a robust security framework with features such as data encryption anonymization, which ensures that the data produced is safe, accurate, consistent, and ethical.

Enkrypt AI enhances this with automated compliance testing, real-time monitoring, and policy adherence guardrails, reducing manual efforts by 90% and minimizing penalties by 20%—ensuring AI remains safe, accurate, consistent, and ethical.

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3. Protection From Model Manipulation

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Threat actors can exploit AI models through data injection attacks or adversarial inputs. Secure AI infrastructures implement red teaming, adversarial training, and guardrails to detect and mitigate these risks.

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4. Secure AI Deployment & Scaling

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Organizations adopting AI at scale need security measures to prevent vulnerabilities from growing with expansion. Zero-trust architecture, continuous monitoring, and DevSecOps practices ensure safe AI scaling.

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5. Business Continuity & Reduced Downtime

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Cyberattacks and security breaches can disrupt AI-powered operations. A resilient AI infrastructure with failover mechanisms and disaster recovery plans minimizes downtime and ensures business continuity.

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How to Secure Your AI Infrastructure from Cyber Attacks in 2025?

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According to a cyber security agency report of Singapore, as of 2024, 25 percent of all AI breaches involved unsecured APIs. Therefore, AI infrastructure security against cyberattacks in 2025 involves a holistic solution set that involves best practices, advanced technology, and regulatory compliance. Below are a few ways to strengthen your AI infrastructure from cyber attacks in 2025:

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1. Multi-Layered Security

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Performing multiple security procedures is essential in securing AI infrastructure against potential threats. Organizations must conduct routine tests and assessments to become aware of an AI system’s vulnerabilities. Further, encrypting AI data, whether at rest or in transit, is crucial regarding data privacy. To add one more protective layer, it is advisable to use IDS to monitor network traffic for anomalies and malicious activities.

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2. Adopting Secure Design Principles

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The ones seeking a secure AI infrastructure must conduct keen risk assessments before installing AI systems. Doing so minimizes the number of attack surfaces. Threat modeling is also vital as it helps predict AI-specific attacks like model inversion and membership inference. Implementing secure coding practices is also essential to avoid vulnerabilities in AI systems.

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3. Securing AI Logic

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Many organizations usually possess combinations of foundation models, embeddings, inference endpoints, and agentic AI workflows hosted internally or on public clouds. To keep such technical treasure unhampered, organizations must secure the entire lifecycle, from build time to run time.

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4. Data Security Posture Management

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AI systems often demand loads of structured and unstructured data to operate. However, it is essential to remember that AI systems shouldn't be exposed to every kind of data but only to the required, sensitive kinds.

Therefore, organizations are advised to leverage DSPM solutions. These solutions help to scan the environment and offer visibility into all data stores.

On top of that, they also help locate data sets for AI compatible with GDPR, CCPA, HIPAA, and other emerging AI-specific regulations. This way, AI infrastructure stays lawful and operates ethically.

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5. RBAC (Role-based Access Controls) and ABAC (Attribute-based Access Controls)

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Enterprises must be cautious about whether or not their data security vendors have RBAC and ABAC controls. The mentioned controls are essential to secure AI infrastructure from cyber attacks as they ensure that every AI model and affiliated employee can only access authorized datasets.

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6. Impose AI Security Standards

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Adopting recognized security protocols and frameworks is very important to mitigate risks related to AI systems. Such AI security standards help immensely in developing, deploying, and maintaining AI applications. For instance, standards like ISO/IEC 27001 ensure that AI systems are manufactured with security in mind.

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7. Consult With Professionals

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Taking the guidance of external experts is a good way to secure your AI infrastructure, as they can offer valuable insights and expertise to strengthen and protect the AI systems. Further, these professionals can efficiently participate in keen security assessments and penetration testing, ultimately helping to mark and fill security gaps.

Are you looking for a comprehensive solution that helps you detect, manage & mitigate all the risks associated with your AI model? Enkrypt AI detects these risks with advanced red teaming techniques and continuously analyses the AI’s behavior in real time. Besides, they also offer automated security controls and real-time guardrails for mitigating these security challenges.

They offer multimodal security solutions, which help organizations with these risks but also help them comply with their models with well-known security standards such as OWASP, MITRE, NIST & EU AI.

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10 Best Practices To Protect AI Models Better

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To secure AI systems from potential cyber threats, professionals must react against vulnerabilities that often hamper the integrity and functionality of these systems. Below are 7 best AI security practices that you should be aware of:

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1. Countering Data Poisoning

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Data poisoning occurs when malicious data enters an AI system’s training pipeline, puncturing the system's reliability and accuracy. Therefore, organizations must strictly prioritize data quality and oversight.

To mark and neutralize threats before they compromise model integrity, organizations are expected to follow validation protocols such as anomaly detection in datasets and monitoring of data pipelines.

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2. Safeguarding Intellectual Property

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Robust authentication measures like multi-factor authentication are crucial to secure system entry points, ultimately protecting AI models from cyber theft. On top of that, using supervising tools offers extra protection as they flag any bizarre access pattern indicative of potential theft.

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3. Ensure Effective and Efficient Sandboxing

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The process of sandboxing refers to exposing GenAI-powered applications to isolated test environments. Further, these applications are scanned to mitigate AI vulnerabilities. It's an efficient practice to avoid cyber threats; however, ensure that the sandboxing environment is ideally configured because if built when hasty, it can backfire in terms of security.

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4. Mitigating Supply Chain Vulnerabilities

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Organizations should properly scrutinize third-party components used in AI systems. Scrutinizing includes vetting datasets and frameworks for vulnerabilities and incorporating supervising tools to detect and possibly cut down potential risks.

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5. Protecting APIs and Endpoints

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Compromising APIs takes a toll on the entire AI system, so safeguarding them is necessary. Implement strong credentials like OAuth(Open Authorization) tokens to authenticate API access.

Further, professionals must restrict excessive requests to APIs to prevent abuse. Lastly, to secure APIs, regularly track API interactions to detect signs of anomalous or malicious activities.

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6. Implement Resource Jacking

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Misutilization of resources, such as unauthorized model training, imposes serious threats and needs to be countered through regular monitoring and strict access controls. This requires AI systems to have alerts configured for anomalous resource usage patterns. This will enable the systems to respond to potential jacking attempts quickly.

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7. Customizing Generative AI Architecture

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To further improve security by focusing on user authentication and log audits, organizations can customize AI architecture according to their convenience and requirements. Meanwhile, ensuring compliance with industry-level standards such as ISO/IEC 27001 is also advisable.

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8. Focusing on Input Sanitization

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While balancing robust security and a seamless consumer experience, imposing specific limitations on user input in GenAI systems is essential. Input sanitization doesn't need to be complicated; it can be simple. For example, organizations can add a dropdown menu instead of textboxes to reduce inputs.

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9. Prioritizing Threat Landscape for AI

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While having data science experts and AI specialists is all good, it is equally important to lay a foundational understanding of the AI threat landscape. For instance, MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a wise starting point that describes various tactics and techniques associated with threat actors. Organizations can analyze the stock and select what's relevant to them to learn from past AI breaches effectively.

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10. Regular Monitoring

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Continuous vigilance is mandatory to protect AI systems from potential cyber threats. Implementing a sturdy system for monitoring AI applications and the infrastructure is necessary as these supervising processes keep track of key performance indicators, model outputs, data distribution shifts, model performance fluctuations, etc.

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Conclusion

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AI offers organizations several advantages to keep pace with the competitive market. However, it is equally important to prioritize the privacy and security of AI systems to avoid or mitigate the risks of cyber attacks. Malicious users are always in the queue to violate vulnerabilities in the AI systems. Hence, it becomes easier to identify potential threats and protect data breaches by taking proper measures and implementing the best AI security practices.

Further, keeping security technologies up to date and regularly aiming to evolve training procedures stretches the future scope of AI and builds trust between enterprises and their consumers. Organizations must constantly engage in a proactive approach by incorporating defensive measures to utilize the benefits of AI models while cutting down the associated risks and challenges.

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FAQs: AI Security

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1. What is AI security?

AI security generally refers to the use of technology and practices to protect AI systems from being violated by malicious users. AI security, hence, primarily ensures privacy protection and secure virtual operations.

2. What is the most significant risk of AI?

There is a pyramid of risks associated with artificial intelligence. Some of the most significant risks include privacy leaks, intellectual property infringement, biased programming, etc.

3. What are some of the common risks associated with AI security?

Among all that exist, some of the most common risks associated with AI security are adversarial attacks, automated malware, supply chain attacks, data breaches, lack of transparency, ethical dilemmas, model theft, etc.

4. How to secure an AI model in 2025?

To secure an AI model in 2025, it is essential to remember where the sensitive data lies and who has access to it. Clear visibility is essential to avoid the possibility of model theft. Some other protective measures include strict data access controls, maintaining data catalog, etc.

5. How to use AI securely?

To use AI securely, implement strong data encryption, access controls, and continuous monitoring to prevent breaches. Use adversarial training and bias detection to enhance AI fairness. Regularly update models, follow zero-trust architecture and comply with privacy regulations like GDPR. Conduct AI red teaming to identify vulnerabilities proactively.