What is AI Security?

AI Security refers to tools, policies, controls, and best practice frameworks designed to protect AI systems throughout their development lifecycle from risks, such as unauthorized access or data exfiltration.

The global tech industry is abuzz with Artificial Intelligence (AI). But AI is more than the new black– it’s the fabric of the future. The technology is expected to add a whopping $15.7 trillion to the global economy by 2030– more than China and India combined.

Considering the significance of AI for powering future technologies, the 2023 Joe Biden Executive Order made it obligatory for federal agencies to designate a Chief Artificial Intelligence Officer (CAIO) responsible for managing AI risks and scaling innovation.

Undoubtedly, AI offers great promises, but the risks it introduces are even greater. From prompt injection and hallucination to risky permissions and sensitive data exposure, these vulnerabilities can compromise data and AI security, undermine ethical integrity, violate legal obligations, and impede innovation. For instance, a study reveals that 57% of organizations limit their AI Copilot rollouts due to oversharing concerns.

In this blog, we dig deep into what AI security is, the real-life impact of the risks associated with AI, and the best practices for strengthening guardrails around it.

What is AI Security?

Artificial Intelligence Security, commonly called AI Security, helps enterprises and security teams protect their AI systems and infrastructure from existing or emerging cyber threats. It is a multidisciplinary field in which experts from various departments work together to identify the risks associated with AI security, governance, and compliance and implement best practices to mitigate them.

The primary aim of AI security is to protect the development lifecycle of AI systems against various vulnerabilities, such as excessive entitlements, training data poisoning, or sensitive data exposure. It is also equally important to ensure that AI works properly regarding ethics, transparency, accuracy, and relevancy.

AI systems comprise various components, with each requiring robust controls and policies against case-specific risks:

AI Models

AI models form the core basis of an AI system. They are a set of algorithms trained on a high volume of datasets to carry out specific tasks. These models recognize patterns via different techniques, make decisions, and forecast outcomes. AI models are susceptible to risks like model theft, adversarial attacks, and model inversion attacks.

AI Agents

If AI models are the brain, AI agents are the body that does all the hard work. In other words, AI agents are autonomous intelligent entities powered by AI models that perceive their environment, perform specific actions, and automate tasks requiring little or no human intervention. Poorly designed agents can make harmful decisions or be manipulated to behave unethically.

AI Knowledge Bases

An AI Knowledge Base is a centralized system or repository that organizes and stores information. AI agents can seamlessly search and analyze the data to make decisions, generate new content, or predict outcomes. However, these systems are vulnerable to data corruption or manipulation, which can lead to data breaches or privacy violations.

Data Pipelines

Data pipeline is the automated process of collecting or extracting data from various sources and ensuring that it is processed and transformed before being loaded into the AI model or knowledge base. The data is then used for ML analysis, training, or fine-tuning the model. Data pipelines with weak security controls are exposed to risks like data poisoning or sensitive data leakage.

Inference Engines

An inference engine is a critical component that applies logical rules to the knowledge base to enable reasoning, produce new information, or make predictions. Inference engines are typically vulnerable to inference attacks or input manipulation, where a malicious input may corrupt the engine’s decision or reasoning capability.

User Interfaces

An AI user interface is where the user interacts with the AI system via prompts and receives an output accordingly. Weak input validation and authentication policies can lead to unauthorized access or sensitive data leakage, while threats like injection attacks can result in the user executing malicious code.

How Exposed is AI to Vulnerabilities?

AI delivers enhanced search capabilities, improved productivity, and scalability. However, without proper controls, these capabilities may attract significant risks. Let’s take a quick look at some instances that serve as a stark reminder of how vulnerable AI is.

Airline Company Fined by a Grieving Customer for Providing Wrong Information

A Canadian airline was fined a couple of thousand dollars because an AI assistant chatbot provided wrong information to a grieving customer. The AI chatbot assistant advised the customer that they could get a discount on purchasing a full-price ticket as a bereavement fare policy. However, the airline service denied the request when the customer requested the claim after taking a return flight. Although sources do not mention the exact reason for such behavior of the AI chatbot, these flaws often happen due to faulty or corrupt training data.

AI Chatbot Suspended for Providing Harmful Healthcare Suggestions

The National Eating Disorders Association (NEDA) designed Tessa, the AI chatbot, to offer users healthcare recommendations. However, the non-profit organization had to suspend Tessa as it was found to be offering recommendations that rather exacerbated patients’ eating disorders.

AI Security Risks from the Lens of OWASP

Discussions around AI security risks initially emerged with the rollout of public-facing systems with integrated Machine Learning (ML) capabilities. However, the concerns gained prominence with the release of Generative Pre-trained Transformers (GPT), revealing newer kinds of risks, like data poisoning and prompt injection.

Lately, a non-profit organization, the Open Worldwide Application Security Project (OWASP), has provided much-needed insights into the prevailing LLM risks. Let’s quickly look at the OWASP top 10 risks for LLMs.

Prompt Injection: This is a technique that a threat actor may leverage to manipulate LLMs' output or intended behavior through direct or indirect natural language inputs. Tay, the AI chatbot, is one such example. Some X (formerly Twitter) users manipulated the chatbot into spewing racist remarks.

Sensitive Data Disclosure: Sensitive data may be exposed when an LLM model is trained or fine-tuned on sensitive or confidential data. This may result in cybersecurity breaches, compliance violations, and reputation loss. It may also be disclosed if a user exposes a sensitive dataset during Retrieval-Augmented Generation (RAG).

Supply Chain Vulnerability: An LLM supply chain comprises third-party packages, training data sets, AI models, and ML frameworks, to name a few. Supply chain security risks may occur when a threat actor exploits any vulnerability at any supply chain instance, such as exfiltrating data or compromising data integrity.

Data and Model Poisoning: Data and Model poisoning risks occur when a malicious actor manipulates training or fine-tuning data, introduces unsafe data during ingestion, or implants a backdoor. This compromises the security and integrity of the data and the model, leading to biased outputs or unauthorized access.

Improper Output Handling: This type of risk occurs when an LLM’s output isn’t efficiently validated, sanitized, or managed. For instance, improper output handling may result in an attacker executing a remote code due to excessive LLM privileges. Or, it may also occur if an attacker gains unauthorized access via an indirect prompt injection attack.

Excessive Agency: This type of vulnerability occurs when an AI agent is given excessive permissions or functionalities. For instance, an AI agent may have excessive permissions to access downstream systems it may not need. Similarly, an AI agent may have access to an extension with functionalities not required by the agent. This type of vulnerability may compromise the confidentiality of the data or the integrity of the model.

System Prompt Leakage: System prompt vulnerability shouldn’t be confused with prompt injection risks discussed above. System prompts are a set of instructions used to control the behavior of a model. Without proper guardrails around system prompts, such as separating sensitive information from the prompts or avoiding reliance, the LLM may be exposed to risks like privilege escalation attacks.

Vector & Embedding Weaknesses: These vulnerabilities are inherent in systems leveraging RAG with LLMs. Security gaps in how the data is ingested, stored, and used in these systems allow malicious actors to manipulate model responses, inject harmful content, etc.

Misinformation: As discussed in the Canadian Airlines incident above, misinformation is a critical vulnerability for LLM applications. These applications are supposed to provide accurate and relevant information. However, due to risks like corrupt data, the application may generate incorrect, inaccurate, or misinformation.

Unbound Consumption: This vulnerability occurs when an LLM’s inference engine is given excessive permission or functionality. It can lead to threats such as model theft and denial-of-service attacks.

Read Here To Learn More About The OWASP’s 2025 Top 10 for LLMs

Why is AI Security Important?

AI adoption is inevitable. The AI market is predicted to grow by a whopping $1.3 trillion in the next 10 years. Ensuring robust guardrails around the development lifecycle of AI is essential for organizations to tap into this growing market, penetrate market opportunities, and fast-track their AI projects.

Data Protection

Data fuels AI. Large Language Models (LLMs) require vast datasets for training and fine-tuning purposes. However, these datasets, available in both structured and unstructured formats, contain sensitive data. Vulnerabilities like prompt injection attacks, data poisoning, and excessive permissions can lead to sensitive data exposure, insider attacks, and data exfiltration. Security controls around data pipelines help organizations ensure the pipelines are AI-ready to fuel technologies like AI Copilots and other enterprise AI applications.

Model Integrity & Reliability

Besides OWASP, MITRE ATLAS™ (Adversarial Threat Landscape for Artificial-Intelligence Systems) has outlined over 60 attacks that can potentially affect AI models. For instance, data and model poisoning or prompt injection attacks can compromise the model's integrity by compromising the model or the data. For a model to be reliable, accurate, and trustworthy, there must be guardrails around the quality of data it is trained or fine-tuned on and model accessibility.

Trust & Adoption

Trust is the essential factor behind the widespread adoption of AI worldwide. Consumers trust AI that is secure, transparent, and ethically sound. However, model hallucination, bias, and toxicity are among the many risks that put off both businesses and consumers. Unreliable AI applications can result in legal scrutiny, reduced user engagement, and reputation loss. Policies and controls around model explainability, data sanitization and validation, and response firewalls can greatly enable enterprises to ensure reliable AI applications that foster trust and adoption.

Compliance Assurance

Stanford University's 2023 AI Index reveals that in the U.S. alone, AI regulations increased from a single regulation in 2016 to 25 by 2023. AI regulations are gaining momentum globally, considering the rapid adoption of the technology and its potential as the cornerstone of the future tech space. Failing to meet compliance requirements, such as the EU AI Act or the 2023 Joe Biden Executive Order, can result in hefty penalties and loss of business reputation and customer trust.

AI Security Standards & Frameworks

AI security standards and frameworks provide an optimal roadmap for enterprises looking to reinforce their data and AI environment. These frameworks offer a set of best practices, recommended controls, and strategies for enhanced security, governance, and compliance. Let’s take a quick look at some of the topmost standards:

• EU AI Act: The European Union’s Artificial Intelligence Act (EU AI Act) is the first and, by far, the most comprehensive legal framework for the development, implementation, and use of AI systems. It sets different enforcement timelines for different categories of AI systems, including unacceptable, high, limited, and minimal-risk AI systems.
• NIST AI RMF: The NIST Artificial Intelligence Risk Management Framework (AI RMF) offers a detailed roadmap for businesses to ensure and promote AI systems' ethical and responsible use and development. The framework promotes a 4-step approach to mitigating risks in AI systems: Govern, Map, Measure, and Manage.
• OWASP Top 10 for LLM: The Open Worldwide Application Security Project (OWASP) is a non-profit organization offering detailed resources and guides on software, web applications, and AI risks. The OWASP Top 10 for LLMs offers insights into the common risks associated with LLMs and the best practices for mitigating them.
• AI TRiSM: The AI Trust, Risk, and Security Management (AI TRiSM) is a framework coined and recommended by Gartner®. The framework provides guidelines and recommendations for identifying and mitigating risks surrounding AI systems' trust, reliability, and security.
• MITRE AI Security Framework: MITRE's Sensible Regulatory Framework for AI Security evaluates AI systems for risks and helps security teams align their AI systems with regulations.

AI Security Recommendations & Best Practices

AI security and governance start with a strategically designed roadmap that helps address the top challenges of safeguarding your AI development lifecycle. The following best practices are meticulously crafted to give organizations a headstart for protecting their AI landscape. Let’s take a quick look.

Discover & Catalog AI Models

The idea here is to get a complete visibility of an organization’s entire AI estate. Security teams must identify all the sanctioned and unsanctioned AI (shadow AI) models, the datasets connected to those models, AI agents, model interactions, and computing resources. Model catalog and discovery go a long way in understanding toxic combinations, thus enhancing risk management.

Assess AI & AI Agent Risks

AI and AI agent risk assessment is crucial for AI security and compliance. Organizations must identify the risks to which AI systems are vulnerable at the pre-development, development, and deployment phases. The models must be rated against security, governance, and regulatory risks, such as toxicity, bias, disinformation, entitlements, and permissions. Based on these risks, teams can determine which AI systems to block and which to reinforce with strict security controls.

Understand Data & AI Relationship

Assessing AI models or agents is just the beginning. Organizations must further understand how data interacts with AI models for training, fine-tuning, or inference purposes. For this purpose, teams must map data and the associated models, SaaS applications, and regulatory frameworks. Security teams must understand the full context surrounding their data and AI models' relationship to enhance transparency, AI explainability, and governance.

Implement In-line Data & AI Controls

Security teams must implement guardrails after gaining insights into the data, AI models, and associated risks. Strict security and privacy controls should be placed around the data that flows into AI models and their generated data (output). For instance, AI models' data should be inspected, classified, and sanitized, ensuring that it aligns with enterprise policies. Similarly, on the output side, context-aware LLM firewalls can be placed to filter out malicious or harmful prompts, retrievals or responses.

Ensure Ethical AI Use & Meet Compliance

Organizations must align their AI systems and applications with relevant regulations and frameworks to demonstrate compliance, such as the EU AI Act or the NIST AI RMF. They must also develop a comprehensive governance framework with a built-in regulatory knowledge base. The framework should also include processes that include running automated checks and compliance assessments.

Check Out the Seven Tests to Ensure Your Enterprise is AI-Ready

Safeguard Your AI & Unlock Its Potential with Securiti

Fast-track your AI development and innovation with Securiti’s AI Security & Governance solution. The solution is designed to help you safeguard your data+AI against the OWASP Top 10 LLM risks and ensure compliance with AI security frameworks and regulations. With the solution enabled, you can:

• Discover and Catalog AI Models: Gain insights into AI models and agents and their use in SaaS applications and public and private clouds. These insights will help you enable strategic policies and controls around risks like excessive agency, overreliance on LLMs, and unbound consumption.
• Assess and Classify AI Model Risks: Evaluate your AI models and agents against various risks and classify the models or agents as per regulatory requirements.
• Map and Monitor Data+AI: Map models to data sources, potential risks, and legal requirements to trace various risks better. This visualization can help you prevent risks like sensitive data disclosure, data and model poisoning, excessive agency, misinformation, and overreliance.
• Enable Data+AI Controls: Implement in-line data and AI controls to mitigate the top security risks outlined by OWASP and NIST.
• Comply with AI Laws and Frameworks: Automate assessments and compliance checks around global AI regulations and frameworks.

Request a demo to see how you can protect your data+AI with Securiti.

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