What Is AI Security? How to Protect AI Models, Data and Agents from Attack

Roughly one in five organizations reported a security incident involving an AI model or application in the past year, up from one in eight the year before, according to IBM's Cost of a Data Breach Report 2026.

So what is AI security? AI security is the branch of cybersecurity that protects artificial intelligence systems from attacks on their models, training data, agents and outputs. It defends against prompt injection, data poisoning, jailbreaks and model theft across the full AI lifecycle. It differs from traditional cybersecurity because the asset under attack is a probabilistic model rather than fixed code.

The attackers are already equipped. 82% of hackers now use AI in their workflow and 74% say it has increased the value of hacking, per Bugcrowd's Inside the Mind of a Hacker 2026 (2,000 hackers surveyed). Defenders have not kept pace: close to seven in ten breached organizations have no governance policy for AI and only two in five apply access controls to their AI models and data (IBM, 2026).

This guide explains what AI security covers, how it differs from traditional cybersecurity, the common AI threats by lifecycle phase, the frameworks that define good practice (NIST AI RMF, ISO/IEC 42001, OWASP Top 10 for LLM Applications) and the controls, monitoring and red teaming practices used to secure AI models and agents.

What Is AI Security? 

AI security is the branch of cybersecurity that protects artificial intelligence systems from attacks. It covers machine learning models, large language models, AI agents, training data and the APIs that expose them. It defends against attacks that manipulate model behavior, extract private data or steal the model itself. NIST's Adversarial Machine Learning taxonomy (NIST AI 100-2, 2025) sorts these attacks into four classes: evasion, poisoning, privacy and abuse.

AI security spans the full lifecycle of an AI system: vetting training data and tracking its lineage, adversarially testing models before release, restricting access at deployment and monitoring inputs and outputs in production for drift, misuse and attack. Attackers can manipulate a model without tripping any conventional alarm, which is why each phase needs its own controls.

It is distinct from AI for security, which uses AI to detect conventional threats like phishing and malware. This guide covers security of AI.

NIST computer scientist Apostol Vassilev, lead author of the taxonomy, put the stakes plainly when it launched:

"Despite the significant progress AI and machine learning have made, these technologies are vulnerable to attacks that can cause spectacular failures with dire consequences."
- Apostol Vassilev, Computer Scientist, NIST. NIST News, January 2024

The taxonomy has since been updated (the 2025 edition adds generative AI attacks), but the four attack classes it named remain the vocabulary the rest of this guide uses.

Unlike traditional security, which focuses on static code and fixed vulnerabilities, AI security accounts for dynamic, data-driven systems that can be attacked in novel ways. The table below breaks down the key differences between AI security and traditional cybersecurity.

Common AI Threats

From training to deployment, AI security is a must-have for every phase of the AI lifecycle. Attacks on AI systems moved from research demos to real incidents in 2023. By 2026 IBM measured model inversion at $6.07 million and prompt injection at $5.89 million per breach, the two costliest AI attack types in its dataset.

• Training phase: Data poisoning happens when attackers manipulate training data to influence model behavior in subtle or malicious ways. It can degrade performance or cause specific failures under certain conditions. Bias injection is a related training-phase attack that deliberately includes bias that skews model outputs.
• Inference phase: Carefully crafted inputs can cause AI systems, especially image classifiers or language models, to make incorrect predictions. These attacks often go unnoticed by humans but can compromise safety-critical applications like medical diagnostics or autonomous vehicles. At this stage, prompt injection attacks can embed malicious instructions into inputs, manipulating the model's responses. Jailbreaks are also a common way to override the AI guardrails developers put in place.
• Deployment phase: Model theft is a big issue during the final stage of the AI lifecycle, where attackers replicate or extract proprietary models by querying them repeatedly. Attackers also exploit endpoints through excessive queries, scraping or injection attacks that abuse your API.

The table below maps the top AI security risks of 2026 to their OWASP Top 10 for LLM Applications entry and the mitigation that closes each one.

Which of these threats apply to your AI system?

The table above is the full menu. Which rows matter depends on what you have deployed: a customer-facing chatbot lives or dies on prompt injection and jailbreak resistance, a model behind a public API is an extraction target, and an agent with tool access turns a bad output into a real action.

Use the threat mapper below to pick your deployment type and see the attacks that apply at each lifecycle phase, the matching OWASP Top 10 for LLM Applications entry and NIST attack class, and the control that closes each one.

●MINDGARD AI Security Threat Mapper

Map the AI security threats to your deployment

Pick the kind of AI system you run. The mapper lists the attacks that apply at each lifecycle phase, the matching OWASP Top 10 for LLM Applications (2025) entry, the NIST AI 100-2 (2025) attack class and the control that closes it.

Customer-facing LLM chatbot RAG app over internal documents AI agent with tools (MCP, email, code) Fine-tuned model behind a public API Computer vision or multimodal classifier Internal employee copilot

How this mapping was built

Threats are drawn from the OWASP Top 10 for LLM Applications 2025 and the four attack classes (evasion, poisoning, privacy, abuse) in NIST AI 100-2 E2025, Adversarial Machine Learning: A Taxonomy and Terminology. Cost figures come from the IBM Cost of a Data Breach Report 2026. The applicability of each threat to each deployment type is Mindgard's editorial judgment, not a published dataset.

Sources: OWASP Top 10 for LLM Applications 2025 · NIST AI 100-2 E2025 · IBM Cost of a Data Breach 2026 · Built by Mindgard

Whatever your deployment type, the pattern is the same: the threats cluster by phase, and the controls that close them are the five practices covered in the rest of this guide.

AI Security Frameworks: NIST AI RMF, ISO/IEC 42001 and the OWASP Top 10 for LLMs

Three frameworks define AI security practice today.

1. The NIST AI Risk Management Framework (AI RMF 1.0, January 2023) organizes AI risk work into four functions, Govern, Map, Measure and Manage.
2. NIST's July 2024 Generative AI Profile (NIST AI 600-1) extends it to LLM-specific risks; Mindgard's guide to the NIST AI Risk Management Framework walks through each function. July 2024
3. ‍ISO/IEC 42001:2023 is the certifiable management-system standard for AI, the AI equivalent of ISO 27001, with controls for AI risk assessment, data quality and impact assessment.

The OWASP Top 10 for LLM Applications 2025 ranks the ten most common LLM vulnerabilities, led by LLM01 Prompt Injection, LLM02 Sensitive Information Disclosure and LLM03 Supply Chain, with Excessive Agency (LLM06) covering over-permissioned agents.

Security teams map controls to all three: NIST AI RMF for governance, ISO/IEC 42001 for auditable process and the OWASP Top 10 for LLMs for engineering test cases. MITRE ATLAS adds the adversary view, cataloguing the tactics and techniques observed against AI systems so red team findings can be tagged the same way ATT&CK tags conventional intrusions.

Best Practices for Monitoring AI Systems

AI security doesn't end at deployment: it requires ongoing oversight. Continuous monitoring confirms that AI models behave as expected in real-world environments while detecting early signs of drift, misuse or malicious probing.

• ‍Set up continuous monitoring: Track model performance over time to detect concept drift (changes in input data patterns) or data drift (shifts in data distribution). Sudden performance drops can be early indicators of tampering or environmental change.
• ‍Establish behavior thresholds: Define clear boundaries for acceptable AI outputs and confidence levels. If the system crosses these thresholds, it should trigger human review or automatic mitigation. Set up alerts for suspicious behaviors, such as repeated edge-case inputs, abnormal query volume or attempts to bypass prompt safeguards. These automated rules help you catch early-stage reconnaissance or adversarial testing.
• ‍Use anomaly detection: Use machine learning-based anomaly detection tools to flag novel attack patterns. Integrate threat intelligence feeds to stay updated on emerging threats targeting specific model types or industries.

Manual monitoring cannot keep up with continuous probing. Organizations use Mindgard to test and monitor AI systems on a schedule, so a model that passed red teaming in January is re-tested after every prompt, model or guardrail change.

AI Model Security vs AI Data Security

AI model security protects the model itself: its weights, architecture, fine-tuning data and inference endpoint.

• The main attacks are model theft (extraction through repeated queries), model inversion (reconstructing training data from outputs), adversarial evasion and jailbreaks.
• Controls include rate limiting and authentication on inference APIs, watermarking, encrypted storage of weights, adversarial training and pre-release red teaming.

AI data security protects the data that flows into and out of the model: training sets, retrieval corpora, prompts and outputs.

• The main attacks are data poisoning, sensitive information disclosure through outputs and prompt-based exfiltration.
• Controls include data lineage and versioning, differential privacy, output filtering and access controls on retrieval sources.

IBM's 2026 report found that only 40% of organizations apply access controls to their AI models and data; 92% of those with an AI-related breach lacked them.

5 Tips for Securing AI Models and Data

Security teams must integrate safeguards across the entire model lifecycle to protect AI systems from real-world threats. Follow these essential strategies to build a more secure AI environment.

1. Train With Verified, Clean Data

Data is your model's foundation. Even the most advanced AI architecture is vulnerable to hidden threats without clean, verified and traceable inputs.

AI models learn patterns, behaviors and relationships from the data they're trained on. If that data has been tampered with, whether through data poisoning, mislabeled inputs or subtle bias injections, then the resulting model may behave incorrectly, unethically or even dangerously.

Worse, these failures may appear only under specific conditions, making them hard to detect through normal testing.

The quality and integrity of your training data directly affect your model's security. Poisoned or biased data can quietly create weaknesses that surface only after deployment.

These vulnerabilities can be incredibly difficult to trace back to their source once a model is live, making early-stage data hygiene a top priority for any AI security strategy.

Stay ahead of these issues by:

• Using trusted data sources: Avoid scraping unverified content from the web or aggregating datasets without vetting them. Use reputable datasets with clearly defined licensing and permissions.
• Scanning for anomalies or bias: Use automated tools and human review to detect skewed distributions, class imbalances and outliers. For example, you should check if the model over-represents one demographic group in the training data for a model that makes hiring decisions.
• Implementing data versioning: Track every change made to your training datasets. Tools like DVC (Data Version Control) or LakeFS help ensure you know exactly what data version contributed to a given model.

2. Invest In Encryption and Privacy Best Practices

AI systems process vast amounts of sensitive data, so encryption and privacy-preserving strategies are required. They form the backbone of secure AI pipelines and help ensure compliance with regulations like HIPAA, GDPR and PCI DSS.

Sensitive data used to train or interact with AI models is a high-value target for attackers. Without strong encryption and privacy safeguards, this data is vulnerable to leakage, theft or exposure through model outputs.

Even anonymized datasets can be reverse-engineered with enough effort, particularly in large language models and generative systems. In regulated industries like healthcare, finance, defense and education, failure to safeguard data can lead to massive fines, legal consequences and irreparable reputational damage.

Follow these best practices to keep your data safe:

• Set up end-to-end encryption: Use strong encryption protocols (such as TLS 1.3 for data in transit and AES-256 for data at rest) to ensure that data is secure during storage and while moving between systems. Encryption prevents interception or theft during routine operations like training, model deployment or API queries. In addition to encrypting raw data, protect the trained models themselves. Model weights and architectures may reveal proprietary algorithms or allow attackers to replicate behavior through model extraction.
• Create differential privacy: This technique introduces mathematical noise into data or model outputs to prevent the identification of any individual data point. It's especially effective for AI models trained on sensitive personal information.
• Use token-based authentication and encrypted APIs: Secure model endpoints with encrypted channels and require API keys or OAuth tokens to restrict and monitor access. Implement rate limiting to reduce the risk of scraping or brute-force attacks.

3. Limit Model Access

AI models are valuable intellectual property. Restricting who can access, modify or interact with your models is critical to preventing theft, misuse or exploitation. Once a model is in production, especially if it's exposed via a public-facing API, attackers may see it as a soft target. Without access controls and usage limits, it's easy to weaponize or reverse-engineer.

Like any other sensitive system, AI models need guardrails to keep attackers (and even well-meaning internal users) from exposing vulnerabilities.

Follow these access control best practices:

• Offer access by role: Assign permissions based on user roles (e.g., data scientists, security engineers, business users). Ensure only designated personnel can retrain, fine-tune or access sensitive outputs.
• Set up authentication procedures: Use API keys, OAuth tokens or SSO integrations to verify every request. Pair this with IP whitelisting or geo-restrictions if models are particularly sensitive.
• Implement rate limiting: Set strict query limits per user or token to prevent scraping, model extraction or brute-force probing.

Steve Wilson, who co-leads the OWASP Top 10 for LLM Applications, told VentureBeat where he would start if he were a CISO reviewing a new deployment:

"The first thing I'd do is put an authorization gate outside the model: the agent can propose changes but cannot grant itself authority."
- Steve Wilson, Chief AI and Product Officer, Exabeam and co-lead, OWASP Top 10 for LLM Applications. VentureBeat, August 2026

Access control for AI is not only about who can query the model; it is about what the model itself is allowed to do.

4. Set Up Filtering and Moderation

Whether you're deploying a chatbot, image generator or large language model, implementing effective filters and moderation tools is critical to keeping outputs safe, ethical and compliant with your organization's standards.

Follow these tips to effectively moderate AI outputs at scale:

• Implement rule-based filters: Start with hard-coded filters that block specific keywords, phrases or pattern types associated with profanity, hate speech, violence or misinformation.
• Moderate AI with AI: Use machine learning models to detect toxicity, bias or policy violations in generated content. These tools are more flexible and context-aware than static rules. Use sentiment analysis or zero-shot classification to flag content that expresses harmful intent or violates content guidelines.
• Create options for human review: Route content to human reviewers for borderline cases or sensitive outputs. This setup adds accountability and builds trust. For example, a financial LLM might require human sign-off for investment or tax strategy advice.

5. Implement Proper Governance and Controls

AI security requires clear policies, transparent ownership and systems that can respond to emerging risks.

Ensure you invest in proper governance and controls through:

• Developing AI-specific incident response: General IT response plans often overlook AI's unique points of failure. Extend your cybersecurity playbook to include procedures for AI-specific threats, such as prompt injection, model drift or unauthorized fine-tuning.
• Creating threat models: Traditional threat models don't fully capture the attack surface of machine learning and generative AI. During risk analysis, identify and assess threats unique to your models, such as model extraction, shadow AI or adversarial manipulation.
• Implementing role-based access controls (RBAC): Access mismanagement is one of the fastest ways AI systems can be compromised. Restrict access to training data, model weights and inference endpoints based on role. Careful access management prevents unauthorized changes and limits exposure.
• Documenting and auditing behavior: Transparency and traceability are key for compliance, incident investigation and continuous improvement. Regularly log and review model inputs, outputs and performance metrics to detect drift, bias or misuse.
• Aligning with standards: Standard alignment boosts trust and prepares your organization for audits and regulatory scrutiny. Follow the NIST AI Risk Management Framework (AI RMF) or ISO/IEC 42001 (both covered in the frameworks section above) to ensure your governance practices meet global expectations.

For teams looking to level up their defenses, explore AI security training courses and resources that cover red teaming, privacy, threat modeling and best practices tailored to modern AI systems.

Securing AI Agents and Agentic Systems

AI agents raise the stakes for AI security because they act, not just answer. An agent that can call tools, browse, read email or execute code through the Model Context Protocol (MCP) turns a prompt injection from a bad output into an unauthorized action.

The OWASP Top 10 for LLM Applications names this risk Excessive Agency (LLM06). Gartner predicts that 25% of enterprise generative AI applications will experience at least five minor security incidents a year by 2028, up from 9% in 2025, with MCP integrations a leading cause.

Securing AI agents means least-privilege tool permissions, an authorization gate outside the model for any high-impact action, isolation of untrusted content (web pages, documents, emails) from instructions, per-agent identities and credentials rather than shared keys, full logging of tool calls and adversarial testing of the agent with indirect prompt injection before it goes to production.

Mindgard's guide to AI agent security risks covers each of these controls in depth.

‍Gartner analyst Aaron Lord was blunt about why the protocol most agents use needs a security review of its own:

"MCP was built for interoperability, ease of use and flexibility first, so security mistakes can manifest without continuous oversight for agentic AI."
- Aaron Lord, Sr. Director Analyst, Gartner. Gartner press release, April 2026

Treat every MCP server and tool an agent can reach as part of the attack surface. Red team the agent with the tools connected, not in isolation.

AI Red Teaming: Test AI Systems Before Attackers Do

AI red teaming is the practice of attacking your own AI system the way an adversary would, before deployment and on a recurring schedule after it. A red team engagement runs prompt injection, jailbreak, data extraction, model inversion and evasion attacks against the model, its guardrails and the application around it, then reports which attacks succeeded and how to fix them.

Automated AI security tools for LLMs and generative AI, including Mindgard's platform, Microsoft PyRIT, NVIDIA Garak and Promptfoo, run thousands of attack techniques against LLMs, agents and multimodal models in hours, while manual red teams probe business logic and novel attack chains.

Red teaming is named as a control in the NIST AI RMF Generative AI Profile and the OWASP Top 10 for LLM Applications.

Mindgard's research team has used it to simulate real-world AI attacks that found failures in production systems, including image safeguard bypasses in ChatGPT.

Trust Your Models by Testing Them First

AI is redefining what's possible, but it's also redefining what's vulnerable. From training data integrity to API abuse, the risks are dynamic, complex and growing fast.

Traditional cybersecurity frameworks aren't enough on their own. Implementing dedicated AI security practices allows organizations to better safeguard their models and the people who rely on them.

These threats cannot be managed by hand: one in four malicious breaches is now AI-enabled. Mindgard is built for this reality. Our Offensive Security platform lets security teams simulate real-world threats, test system resilience and uncover vulnerabilities before attackers do, with red teaming that runs continuously rather than once. When it comes to AI security, waiting until something breaks is no longer an option.

It's time to pressure-test your security measures: Book a Mindgard demo now to identify where your AI is vulnerable.

Frequently Asked Questions

How is AI security different from securing traditional software?

Traditional software security focuses on fixed code and known vulnerabilities. AI security must account for dynamic learning systems, unpredictable outputs, and threats like model inversion or adversarial prompts, which have no direct analog in traditional software.

Can AI models be reverse-engineered by attackers?

Yes. Attackers use methods like model extraction or inference attacks to replicate or learn sensitive characteristics of a model, especially if rate limits or access controls aren't in place.

What types of AI systems are most vulnerable to attacks?

Common targets are large language models, computer vision systems, and models deployed via public APIs. Open-access models with weak safeguards are especially at risk for prompt injection, jailbreaks, or data leakage.

What is the difference between AI security and AI for security?

‍AI security (security of AI) protects AI models, data, agents and applications from attacks such as prompt injection, data poisoning and model theft. AI for security uses AI and machine learning inside security tools to detect phishing, malware, anomalies and intrusions faster. The two overlap in a security operations center that uses AI to defend AI systems, but they are separate disciplines with separate controls. This guide covers security of AI.

Why is AI security important?

‍AI systems now make decisions, handle regulated data and take actions through agents, so a compromised model is a compromised business process. IBM's Cost of a Data Breach Report 2026 puts the average cost of an AI-enabled breach at $6 million, about $1 million above the global average, with model inversion ($6.07 million) and prompt injection ($5.89 million) the costliest AI attack types.

Regulators are also moving: the EU AI Act's obligations for high-risk systems and ISO/IEC 42001 both require documented AI risk controls.

What are examples of AI security?

‍Examples of AI security controls include:

• Red teaming a large language model for jailbreaks before launch
• Filtering prompts and outputs for injection and data leakage at runtime
• Applying rate limits and authentication to a model API to stop extraction
• Versioning and auditing training data to catch poisoning
• Giving an AI agent least-privilege tool permissions
• Monitoring a production model for drift and abnormal query patterns

Examples of AI security incidents include:

• Indirect prompt injection through a web page an agent reads
• A jailbreak that bypasses content safeguards
• A model inversion attack that recovers private training records

What is AI security posture management (AI-SPM)?

‍AI security posture management is a category of tools that discovers every AI model, agent, dataset and API in an organization (including shadow AI) and continuously checks their configuration, permissions and exposure against policy.

AI-SPM answers the inventory and misconfiguration side of AI security; AI red teaming and runtime protection answer the adversarial side by testing and defending how the systems behave under attack.

Is AI security a good career?

‍Demand for AI security skills is rising with adoption: IBM reports that 85% of breached organizations plan to increase spending on security tools and governance after learning about frontier AI cyber capabilities.

Roles include AI red teamer, ML security engineer, AI governance lead and AI security researcher.

Relevant credentials include ISACA's Advanced in AI Security Management (AAISM) and the Certified AI Security Professional (CAISP), plus hands-on practice with the OWASP Top 10 for LLM Applications and MITRE ATLAS.