AI Security – Risks, Practices, and How to Protect AI Systems

You get a call from your CFO who sounds panicked.

“Can the new AI pilot leak our pricing models?” he asks.

You think the answer is no.

Then a red team does a demo that shows how they were able to extract the sensitive pricing data out with a single prompt.

That’s AI security in 2025 for most organizations.

What is AI Security?

AI security are the practices and principles that protect AI systems and the unique attack surfaces they introduce against threats or misuse by malicious actors to minimize risks associated with their use. It involves protecting the AI model(s) used, the data used to train them, the infrastructure they run on, and monitoring the safe use of those systems in production.

As the capabilities of AI evolve, their attack vectors and potential security issues will expand with them. Those attack vectors and security issues are generally different from those found in traditional software.

Our team here at The Provato Group really emphasizes the importance of data privacy, protection, and bias testing to prevent the most egregious AI security risks for our clients.

What Does “AI” Mean in a Security Context?

In the context of security, AI means learning systems with adaptive decision logic, autonomous behavior, and critical data dependencies that go beyond traditional software to fundamentally shape how these systems think and act.

Conventional applications follow predetermined rules created by the developer. AI systems are trained and develop their own rules. The learning process AI uses can be influenced by malicious actors, manipulating them to produce outcomes that align with their goals versus those of the organization that own the application.

The AI training process creates security concerns that never existed before. Beyond just the initial model building, AI systems require protection throughout their entire data lifecycle to ensure reliable predictions and outputs. On-going protection is a requirement because when an attack of exploit occurs, it can lead to serious and immediate consequences.

Why is AI Security Important?

AI security is important because these systems and models are being integrated into critical business infrastructure at a rapid pace. As adoption grows, so does the attention it receives from attackers and malicious actors.

If systems are attacked and successfully exploited they can leak private and proprietary business data. Attackers can bias AI models to produce outcomes favorable to their goals. That can lead to massive financial loss from fraud, increased costs, and in some cases legal repercussions.

Educating your workforce on how the AI should be used and providing transparency into how it works can help head off small problems before they become much larger security vulnerabilities.

How Is AI Used in Cybersecurity?

AI is used in cybersecurity to evaluate massive amounts of rapidly incoming data to identify abnormal or malicious patterns, detecting threats in real-time, and automating the incident response to those security vulnerabilities. Generative AI powers the real-time threat detection through pre-defined automated response workflows which significantly reduce the amount of time between vulnerability or attack identification and mitigation. Machine learning systems learn over time what normal network and user behavior is, and are able to notify administrators when deviations from those patterns occur.

How Does AI Improve Cyber Security and Threat Response?

AI improves cybersecurity and threat response by automating threat and attack detection at scale, triaging alerts while reducing false positives, and accelerating incident response by leveraging pre-defined workflows. AI is superior to signature-based systems because it is able to identify both known and unknown threats, actively detecting abnormal behavior and blocking malware or exploits before they are executed.

In a 2024 report by IBM ‘110+ of the Latest Data Breach Statistics to Know for 2026 & Beyond’ organizations that extensively deploy AI in cybersecurity contained a data breach 80 days faster and saw cost savings of nearly $1.9 million compared to organizations with no use. The same research has shown that 1 in 6 breaches in 2025 were AI-driven, which points to a secondary benefit of using AI in cybersecurity.

In Tao Ban’s 2021 study ‘Combat Security Alert Fatigue with AI-Assisted Techniques’ it was shown that with the use of AI, positively identified threats jumped from 593 to 2,239 (nearly 4x) from the same sample set of 133.77 million alert log lines. Additionally, there was a 99.999% reduction in false positives, with a 99.598% recall rate for catching critical incidents. Other findings from the study were that AI assisted cybersecurity had a precision of 99.687%, false positive rate of just 0.001%, and F-score of 99.643%. All of this leads to AI also improving cybersecurity by reducing the operational strain placed on security teams, effectively combating security fatigue as threat volumes and complexity increase.

How Does AI Combat Security Fatigue?

AI combats security fatigue by reducing the cognitive load on already overstretched security analysts by helping to eliminate the constant need to manual alert review and investigation. Since AI handles those repetitive tasks, human security analysts can focus on complex scenarios and high impact work. IBM’s 2024 research shows that shorter incident lifecycles and reduced reliance on scarce human expertise directly reduce analyst burnout, allowing teams to work through rising threat volumes and complexity.

Tao Ban’s study found that this problem is widespread across the industry. 31.9% of IT security analysts no longer pay attention to alerts due to the high frequency of false positives. This isn’t surprising when you take into account that 50% of enterprises have six or more tools generating all of those alerts for review.

How Secure is AI?

AI is not inherently secure by itself. It is difficult to secure well due to its nature and how different it is from traditional software. Security protocols and systems that are typically used are unable to address the unique attack surfaces and vulnerabilities that AI systems have.

Currently, its security depends on a multilayer approach to frontline defense and vulnerability mitigation. As the field matures, new defenses and evaluation methods are always being created.

What Are the Biggest AI Security Risks?

The biggest AI security risks are:

1. AI Data Security and Privacy
2. AI Code Vulnerabilities
3. Agentic AI Systems

AI Data Security and Privacy

AI data security and privacy risks refer to the vulnerabilities in how AI systems collect, process, and store sensitive information. Machine learning and generative AI systems carry related, but different risks from data breaches. In the event of a breach, generative AI systems can learn and reproduce personal data that was in their training corpus, while machine learning systems maybe produced biased outcomes if there are flaws in their training data. Both can lead to potential violations of personal privacy or discrimination.

The limited opacity of some AI systems makes it difficult to determine when there is a algorithmic bias or personal data is being mishandled. Risks assessments must be carried out to account for if and when AI outputs create ethical concerns that need to be addressed.

Our AI developers strive to prevent these risks by enforcing data governance and handling principals. The team here puts a lot of emphasis on AI outputs remaining fair and ethical while making sure that private information is always protected.

AI Code Vulnerabilities

AI code vulnerabilities are flaws in the applications code that a malicious actor can exploit or otherwise take advantage of. These vulnerabilities are found in both in human, hand-written code as well as AI-generated code.

AI-assisted programming and vibe coding can make assumptions or introduce insecure patterns into the code unless proper reviewed and approved by human developers. Generative AI has in many ways changed the face of programming, but create weaknesses that traditional cyber security practices may not be equipped to notice immediately. Effective cyber security for AI code vulnerabilities requires treating all code as untrusted no matter the source to be sure it undergoes some form of review and testing throughout the application’s lifecycle.

Research from the paper An Empirical Study on the Effectiveness of Security Code Review recommended that organizations should use multiple independent reviewers instead of individuals, as no single developer in the study found all vulnerabilities. The paper allow promotes the use of tools to help identify problems human reviewers may miss initially. For developers the researchers recommend they should try to keep code changes small, and easy to review.

Agentic AI Systems

AI agent security refers to specialized strategies and practices used to protect autonomous AI agents from malicious attacks and unintended outputs. Agentic AI security is challenging due to the unique vulnerabilities caused by agents’ completely autonomous workflows, making independent decisions and interacting with external systems often with no human oversight.

AI agents present different security challenges depending on their architectures. Single-agent architectures concentrate all tasks into one system creating a high-value target that, if compromised, will give the attacker access to the full range of capabilities and data. Multi-agent architectures spread their capabilities across multiple agents which can help contain a compromise, but by attacking inter-agent communication, message passing, and coordination mechanisms—relationships between agents can be exploited.

How Are AI Systems Attacked?

AI systems are attacked in ways that traditional software isn’t, which include the following:

1. Data poisoning or data manipulation attacks
2. Model inversion and inference attacks
3. Adversarial, evasion, and transfer attacks
4. Prompt injection attacks
5. Supply chain attacks

Data Poisoning and Data Manipulation Attacks

Data poisoning, or data manipulation attacks against AI systems occur when a malicious actor injects misleading data into an AI’s training dataset, or when they alter input data that leads to incorrect decisions by a machine learning model.

For example, a facial recognition backdoor could be built with modified images that cause certain people to be mislabeled. Hypothetically, they could have the model learn to label “Person A” as “Person B” whenever Person A is wearing glasses which can lead to unauthorized access to systems and physical locations.

AI systems rely heavily on data integrity, and even small manipulation attacks can cause system-wide issues.

Model Inversion and Inference Attacks

Model inversion and membership attacks occur when an attacker reverse-engineers a model’s outputs to recreate and extract sensitive data. The attacker will repeatedly probe the model, exploiting discrepancies between the model’s outputs and underlying data distribution to infer private or confidential details in the training dataset. The goal of these attacks are to recreate the dataset.

These attacks expose personal data, proprietary datasets, or confidential business information, creating privacy violations and regulatory risk even when direct access to training data is restricted.

Adversarial, Evasion, and Transfer Attacks

Adversarial and evasion attacks create input designed to trick the model causing it to misclassify or misinterpret data. Transfer attacks extend this risk by applying successful exploits from one model to another with similar decision boundaries.

A common example is altering a stop sign with small visual modifications that cause an autonomous vehicle to misread it as a speed limit sign. Because many models share architectural similarities, attackers can reuse these techniques across systems.

Prompt Injection Attacks

Prompt injection attacks disguises malicious code or inputs as safe-seeming to get a LLM to leak sensitive data, cause system bias, or generate harmful content. These attacks may be direct, indirect through third-party data sources, or persistent when malicious prompts are stored and reused across sessions.

Creating custom GPTs can seem like a cost-savings option, but they are significantly more vulnerable to prompt injection attacks than custom developed solutions like our team produces.

The 2023 study ‘Assessing Prompt Injection Risks in 200+ Custom GPTs’ found that system prompt extraction had a success rate between 88.54%-95.31%, and roleplay jailbreaks were 90.62%-98.70% successful against user-created GPTs. Additionally, attackers could extract sensitive information from uploaded files by exploiting how GPTs reference these files as part of their knowledge base.

Supply Chain Attacks

Supply chain attacks target the data, software, and operations used to train, create, or implement AI systems. Those include training data, pre-trained models or libraries, APIs, and update pipelines.

The 2024 paper ‘Securing the AI supply chain: Mitigating vulnerabilities in AI model development and deployment’ identified that AI systems are far more attack surfaces than traditional software. Our team fights back against supply chain attacks like this by using holistic, layered security with runtime anomaly detection and real-time monitoring for high priority applications that is recommended by the researchers of this paper.

What Are the Security Implications of Generative AI Systems?

The security implications of generative AI systems mean increased threats due to attack surface expansion, while also introducing new vectors such as data poisoning and deepfake social engineering. Generative AI has made sophisticated attacks accessible to anyone while also greatly increasing the speed and scale of those attacks.

This is backed up by 2023 research by Clark W. Barrett in ‘Identifying and Mitigating the Security Risks of Generative AI. ‘ Before generative AI cyber attacks were a time-consuming, costly, high effort process for threat actors. They had a high barrier of entry due to the technical expertise required, and significant engineering effort to execute. After generative AI those same attacks could be carried out with minimal human involvement with lower costs. The pool of potential adversaries has grown dramatically as a result.

Barrett’s research showed that generative AI broke previously dependable detection paradigms. Because it creates coherent, conversational communication, clumsy old Nigerian scams and poor grammar are now meaningless indicators. GenAI is able to create industrial-scale personalization to carry out authentic, context-aware targeted social engineering attacks. Digging deeper, false information is now easily spread through the creation of realistic video, audio, or image deepfakes. The paper was able to illustrate how security is now a socio-technology problem where trust in content, communication, and code can not be taken for granted. That erosion of trust extends to fundamental concerns about how generative AI systems themselves manage sensitive data.

How Do Generative AI Models Handle Privacy and Data Security?

Generative AI models do not guarantee privacy or data security. Instead those protections are implemented through a combination of design choices, architectural techniques, and operational controls applied during training, deployment, and over the entire lifecycle of the solution. The individual techniques and how they are implemented widely vary across the industry.

Common privacy-preserving techniques include differential privacy, federated learning, and homomorphic encryption. According to the 2024 study ‘Privacy and Security Concerns in Generative AI: A Comprehensive Survey’ by Abenezer Golda, differential privacy adds noise to data to protect individual information, federated learning keeps raw data on local devices rather than centralizing it, and homomorphic encryption enables computations on encrypted data without exposing the underlying information. These techniques come at a cost though. Differential privacy degrades data quality, federated learning requires high communication overhead, and homomorphic encryption demands enormous computational resources. The same research also demonstrates that when models must also employ adversarial training, input validation, and continuous monitoring.

Adversarial attacks continue to evolve faster than defense mechanisms can thwart them, and there’s no single comprehensive security solution that fully address privacy and data security. As a result, privacy and data security in GenAI remain risk management concerns, and not problems that can be solved by technical means alone. Security teams should regularly conduct vulnerability tests on their generative AI systems for operational awareness as new threats emerge.

How Do Companies Test Generative AI for Security Vulnerabilities?

According to the 2024 paper ‘Red-Teaming for Generative AI: Silver Bullet or Security Theater?’ by Michael Feffer identified three complementary testing methods that companies can use to meaningfully improve GenAI security:

1. Deep Evaluations By Expert-led Teams
2. Crowdsourced Red-teaming
3. Automated, AI-powered Testing

First, expert-led teams conduct evaluations lasting 6-7 months with full model access, allowing specialists to uncover attack vectors that automated tools miss. Second, crowdsourced red-teaming uses evaluators from platforms like Amazon Mechanical Turk or events like DEFCON’s AI Village to identify unexpected edge cases and real-world usage patterns. Third, automated AI-powered testing uses language models to generate thousands of adversarial prompts at scale, stress-testing safety guardrails more aggressively than manual approaches.

Red-teaming has become the industry standard approach to testing for vulnerabilities, but current practices have inconsistent methodologies, resources, and accountability. These inconsistencies turn red team tests into security theater versus providing helpful insight and protection in many cases.

Will AI Replace Cybersecurity Jobs?

No, AI won’t fully replace cybersecurity jobs, but it will automate many tasks and change the required skills of tomorrow’s security analysts. According to a 2024 paper ‘Incorporating the Future: Optimizing Cybersecurity through Seamless Integration of Artificial Intelligence’ by Muhammad Asif Ibrahim, human expertise remains indispensable in cybersecurity operations. While AI excels at processing huge amounts of data and identifying potential threats, they frequently generate false positives that require human interpretation. These false positives are hard to remove from AI cybersecurity systems because generative AI uses non-deterministic logic, so we never truly understand why it produces the alerts it does.

As a result, cybersecurity professionals will see their roles change to better collaborate with AI systems. Current skill gaps that need to be addressed include:

• Interpreting and validating AI-generated alerts
• Working with developers to reduce false positives by fine-tune AI models
• Understanding the logic behind AI decision-making
• Integrating AI tools with existing security infrastructures
• Providing ethical oversight and contextual judgment on the AI systems used

When looking over that list, one thing becomes very clear. Analysts will need to deeply understand the core principals of AI security.

Can AI Replace Cyber Security?

No, AI will not replace cybersecurity, but it will augment the work of analysts moving forward. Artificial intelligence (AI) can help automate tasks like threat detection and incident management, but there still needs a human in the loop to handle threats like zero-days or advanced attacks. AI struggles in situations where nuance, context, or ambiguity exist. For example, if an employee is traveling and logs in from a different time zone, an AI system may falsely flag that as a malicious login attempt whereas a human will be able to evaluate the situation correctly based on the larger context.

With that having been said, humans simply can not beat AI when it comes to processed huge quantities of data, and the rate of analysis that occurs. This matters as the number of new threats grows every day. According to the 2020 paper ‘Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity’ by S. Zeadally the German AV-TEST GmbH research institute registers more than 350,000 new malware programs daily, which breaks down to 266 types of new malware every minute. Fortunately, machine learning has accuracy rates when it’s used to fight back, demonstrating 99% accuracy in detecting DoS (denial-of-service) attacks, 98.86% accuracy in identifying zero-day phishing attacks, and 93% accuracy in detecting hate speech.

The ideal approach will be to combine the judgement of analysts with the raw computational power of AI, not replace the humans. Keep in mind though, analysts will need to learn the core principals of AI security no matter how accurate or powerful AI systems are.

What Are the Best Practices of AI Security?

The best practices of AI security are:

• Data protection and privacy
• Zero-trust security framework
• Security by design
• Threat detection and mitigation
• Human oversight and transparency
• Continuous monitoring
• Secure data management

When thinking about best practices as it applies to AI security, it’s important to shift your thinking from how it is approached with traditional software to how it needs to be handled with a system that learns and evolves with data over time. The data itself is a living asset for your model. Data can be leaked or exploited at any time during training, intermediate states, and production. It’s not like locking down a database, but rather understand that the model can learn or provide sensitive information in unexpected ways. That’s what makes robust, zero-trust security frameworks so important in AI. Not everyone in your network should be able to see everything. You should consider role-based controllers and multifactor authentication (MFA) as non-negotiables. That means you have to implement security on day-one, and not try to bolt it on later.

During the entire lifecycle of the AI solution, you need to be thinking about AI specific attack vectors along with traditional application security concerns. Continuous monitoring is needed to because malicious actors can attempt to poison your training data, exploit outputs, or reverse engineer your models at any time. It’s important that you keep a human in the loop to be on the lookout for abnormal or unexpected outputs. They can be signs of an attack, or they can be a warning signal that the model needs to be reviewed for biased outputs that violate ethical standards.

Security Best Practices and Responsible AI

Security is a component of responsible AI. Beyond just protecting against threats, security must also consider the ethical implications of AI use. Part of your ongoing security efforts will include actively working to identify and mitigate bias in training data and model outputs, ensuring fairness across different user groups, and maintaining transparency. Responsible AI practices directly strengthen your security posture. Building ethical AI systems and secure AI systems aren’t separate tasks, but rather two sides of the same coin.

How To Secure AI Systems

AI systems are secured by implementing controls across the entire solution lifecycle and infrastructure, from data collection and learning to the physical hardware that runs the AI models themselves. Your strategy for AI security must include:

1. Data Security
2. Model and Infrastructure Security
3. Runtime and Operational Security
4. Governance, Monitoring, Accountability, and Training

AI security frameworks help organizations manage and prevent adversarial attacks, bias, and comply with both existing and future regulations. According to the 2025 paper ‘Enhancing Data Security in Artificial Intelligence Systems: A Cybersecurity and Information Governance Approach’ by Akinde Michael Ogunmolu, mixed‑methods framework applied to 11,245 AI deployments (including BERT, YOLOv7, and federated learning) raised a Composite Security Score to 0.87 vs. 0.78–0.82 for prior models and achieved 84–89% attack mitigation for threats like data poisoning, adversarial examples, and model inversion, plus 90–95% regulatory compliance (GDPR, NIST RMF). This is further supported by the 2025 paper ‘The Convergence of AI Governance: A Framework for Privacy, Security, and Model Management’ by Bharat Veeranjaneya Reddy Devagiri that found organizations with integrated AI privacy‑security‑model‑governance frameworks report 31% fewer data‑privacy violations, 43% fewer security vulnerabilities, and higher long‑term model accuracy (≈62% higher), indicating better protected pipelines and model integrity.

How Can We Implement AI Security in the Enterprise?

Implementing AI security in enterprise environments requires establishing organizational accountability and governance structures. They will define approval workflows for model deployments, establish risk thresholds that trigger additional review, and create escalation paths when security requirements conflict with business timelines or model performance. These processes must integrate smoothly with existing Ops processes rather than creating parallel systems. AI security should be viewed as a shared role, with product teams leading data quality efforts while central governance focuses on policy enforcement, third-party risk management, and maintaining visibility.

What is AI Security Governance and Responsibility?

AI Security Governance is the framework of policies, procedures, and controls used to oversee AI systems while responsibility represents that accountability across the AI lifecycle. Effective governance requires defining three elements, each with questions that must be answered and failure modes that occur when they’re left ambiguous:

Can You Integrate AI Cybersecurity Platforms with Enterprise Security Tools?

Yes, you can integrate AI cybersecurity platforms with existing enterprise security tools. The majority of the integration work is embedding AI capabilities directly into existing Security Operations Center (SOC) workflows (SIEM, SOAR, and EDR systems) to automate threat detection, investigation, and response. Tool selection should prioritize integration capabilities with extensive APIs, SSO support, and compatibility with enterprise standards like SAML, SCIM, and common logging formats.

What Are Cloud and Endpoint AI Security Considerations?

AI plays a significant role in modern cloud security, but its security characteristics and risks shift across environments. AI secure cloud environments through:

• Threat detection and anomaly analytics
• Automated vulnerability scanning and response
• AI-powered DevSecOps and CI/CD
• Cloud-AI specific protections

Each environment presents unique security challenges that require different protection strategies. The following table outlines how AI security considerations vary across different cloud and endpoint architectures:

Organizations must understand the urgency of cloud security. Some sobering statistics found in the 2024 paper ‘AI-Driven Proactive Cloud Application Data Access Security’ by Priyanka Neelakrishnan are that 68% of organizations observed an increase in insider threats over the past year, yet 49% cannot detect them. Compromised cloud accounts cost companies an average of $6.2 million annually and lead to 138 hours of application downtime. 62% of data breaches are attributed to leveraged credentials. It takes an average of 287 days for an organization to identify a data breach, with an average cost of $3.86 million per incident.

What Are the Regulatory and Compliance Considerations for AI Security?

AI security is no longer just a technical problem. As the field has evolved and AI adoption increased, it has become a growing legal and regulator concern as well. More often than not, external obligations are shaping what secure AI will look like, which can change based on jurisdiction and the industry. Some of the key external requirements vs security implications are:

Security by design and AI-specific risk assessment have growing become explicit regulatory requirements, but the methods used for testing and measuring those things are still vague and lack uniformity. Robustness, privacy, auditability, and explainability are becoming legal duties for organizations, not optional best practices for developers. Companies need to prepare for the day when they must map auditable governance to demonstrate security compliance at scale.

What is an AI Development Company’s Role in Security?

When creating a new AI solution, an AI development company is responsible for the secure design, development, deployment, and maintenance of the model.

Cross-team collaboration is important to fulfilling the security needs of AI solutions. No matter how strong a developer’s commitment is to security, business needs regarding project speed or budget represent challenges that can’t be overcome by just one team.

It is important that the end administrator (the developer or the client) maintain controls, monitor for threats, and enforce responsible AI best practices during use. This can be done with shared tools, or a committee that includes representatives from all concerned business units.

What is Our Approach to Securing AI?

AI security isn’t an afterthought for us. We build it into every phase of a development project as a foundational requirement. Our approach combines secure model design with protected infrastructure to identify and address vulnerabilities before they become larger problems.

Our teams follow established secure coding practices and work to stay informed about new and emerging threats. We have clear procedures for assessment and response, and we keep our clients informed about the status of their systems.