What is AI Security?

Artificial intelligence (AI) is transforming how organizations operate, innovate, and solve complex challenges. It is also increasing the speed, scale, and precision of cyber-attacks. This page explains what AI security is, why it matters, and how organizations can address the risks AI introduces across applications, data, infrastructure, and business processes.

An AI security program helps organizations:

• Securely adopt and operationalize AI by addressing the expanding attack surface, governance challenges, and emerging risks created by AI-driven applications, workflows, and threats.
• Protect against the rapidly evolving risks introduced by AI, including securing AI systems, managing AI-generated risk, and defending against increasingly sophisticated AI-enabled attacks.
• Establish the governance, visibility, and technical controls needed to ensure risk does not outpace the organization's ability to manage it.

AI security spans three interconnected domains:

• Securing AI applications and systems to enable secure innovation.
• Accelerating cybersecurity operations with AI to improve detection, response, and decision-making.
• Defending against AI-powered threats that increase the speed, sophistication, and scale of attacks.

Who Does AI Security Impact?

AI security is not exclusively a “security team” problem. It touches every function that builds, deploys, governs, relies on, or uses AI systems. Any role that touches AI systems, AI-generated outputs, or AI-influenced decisions has a stake in AI security outcomes. Understanding where each person in the organization fits helps clarify where to focus.

Executive Leadership

• CISO: Owns the overall AI security strategy, risk assessments, red teaming, and threat preparedness across the organization.
• CIO and CTO: Accountable for secure AI adoption decisions, enterprise architecture, and ensuring AI initiatives align with the organization's security posture.
• Chief Data Officer and Chief Risk Officer: Responsible for AI governance, data privacy, risk quantification, and regulatory compliance related to AI systems.

Security and Technical Teams

• Security architects and engineers: Design and implement controls for AI applications, agents, and infrastructure.
• Application security teams: Assess AI-specific vulnerabilities, conduct adversarial testing, and secure the AI development lifecycle.
• SOC and incident response teams: Detect and respond to AI-enabled threats, manage AI-specific playbooks, and operationalize AI-driven detection tools.
• Identity and access management teams: Govern non-human identities, enforce agent permissions, and manage lifecycle controls for autonomous systems.

Development and Data Teams

• Software developers and MLOps engineers: Build AI applications, manage model pipelines, and integrate security into AI development workflows.
• Data science and ML engineering teams Train, validate, and monitor models while ensuring data integrity and preventing model poisoning or drift.

Governance and Compliance

• Governance, Risk & Compliance (GRC), legal, and privacy teams: Align AI use with regulatory requirements, draft acceptable-use policies, assess third-party AI vendor risk, and manage compliance obligations.
• AI governance leads: Establish intake processes, maintain AI inventories, and ensure organizational policies keep pace with AI adoption.

AI Users

• Business professionals: Daily AI assistant and generative AI users whose interactions determine whether sensitive data leaks into unapproved platforms.
• Non-technical "vibe coders": People building applications with AI tools without formal dev training, potentially deploying insecure AI-generated code.
• Customer-facing teams: Handle personally identifying information (PII) and financial data through AI workflows for outreach, chatbots, and service automation.
• Executives and decision-makers: Consume AI-generated reports and recommendations; implicit trust without validation can create strategic risk.
• Contractors, vendors, and partners: Extend the governance perimeter beyond internal controls through third-party AI tool access.

What are the Primary AI Security Challenges Organizations Face?

AI Is Accelerating Risk and Risk Awareness Faster Than Security Can Adapt

AI accelerates vulnerability discovery, software development, and attacker capability simultaneously. Security teams face a widening gap between the speed of AI-driven change and their ability to respond. Incumbent tools were not designed to handle AI attack surfaces, and many security policies and playbooks lack the capabilities to respond to rapidly evolving threats.

Shadow AI and Ungoverned Adoption

Employees routinely adopt AI tools outside approved governance and compliance processes. Often, generative AI access happens through personal accounts that fall entirely outside identity management systems. Many of those AI interactions involve sensitive data, with the employees believing that, because they’re signed into a personal account, that data is secure. The reality is that proprietary and private data entered into a public AI solution can inadvertently become publicly visible or can be used to train public AI models. Without visibility into how AI is being used and enforceable acceptable use policies in place, organizations cannot protect what they cannot see.

The Expanding Attack Surface

AI does not simply add a new category of risk. It amplifies existing risk across multiple areas:

• AI-generated code introduces insecure patterns and hallucinated APIs that developers may not catch before deployment.
• Open-source dependencies expand supply chain risk as AI-driven development increases reliance on third-party components.
• CI/CD pipelines accelerated by AI can propagate security flaws into production faster than ever.
• Autonomous agents make decisions and take actions at machine speed with broad system access, often without behavioral governance or oversight mechanisms.
• Internal AI projects introduce new data exposure risks and governance challenges with every deployment.

AI-powered Threats Are Scaling, Evolving, and Becoming Increasingly Sophisticated

Cybercriminals leverage AI to automate, scale, and enhance attacks. Generative AI enables hyper-realistic phishing, automated social engineering, deepfake impersonation, and AI-powered malware. AI transforms cyber criminal operations from one-to-one to one-to-many, greatly expanding operational scale while reducing costs. Low-skilled actors now have access to high-impact tools that previously required seasoned adversaries.

Autonomous Agents Operate Beyond Traditional Controls

Security models built on the “authorized versus unauthorized” distinction struggle to govern AI agents. Agents operate entirely within the authorized category, carry no personal accountability, and bypass many assumptions that serve as a foundation for traditional identity and access management. An agent can have human-level access breadth while making contextual decisions at machine speed. When an autonomous system produces an unintended outcome, every identity check may pass and every access control may work as designed, yet the result can be detrimental.

Governance Gaps and Compliance Pressure

Organizational policies, oversight mechanisms, and risk management processes often fail to keep pace with AI adoption. Regulations like the EU AI Act, NIST AI RMF, and ISO/IEC 42001 are creating new requirements for risk-tier classification, model lifecycle documentation, bias testing, and transparency. Without clear governance, organizations face increased exposure to security incidents, regulatory violations, and inconsistent AI use across the enterprise.

Implicit Trust in AI Outputs Increases Risk

People often assume AI-generated outputs are accurate and safe. This creates risk when incorrect, biased, or insecure recommendations go unchallenged. Employees may accept flawed recommendations, approve insecure code, or make business decisions based on information that is incomplete, inaccurate, or lacking context.

What Does the AI Regulatory Landscape Look Like?

Governments and standards bodies are rapidly establishing requirements for AI governance, risk management, and security. Organizations operating across industries and geographies must navigate an evolving patchwork of regulations and frameworks. Here are the most significant:

Global Standards and Frameworks

• EU AI Act: The first comprehensive AI-specific regulation. Classifies AI systems by risk tier (unacceptable, high, limited, minimal) and requires conformity assessments, transparency reports, and ongoing monitoring for high-risk systems. Applies to any organization offering AI products or services in the EU.
• ISO/IEC 42001:2023: The first international AI management system standard. Provides a structured framework for establishing, implementing, and continuously improving AI governance within an organization.
• NIST AI Risk Management Framework (AI RMF): A voluntary U.S. framework that guides organizations through AI risk identification, measurement, and mitigation. Covers model lifecycle documentation, bias testing, and provenance tracking (SBOM/MBOM).
• ISO 27001 + ISO 42001 alignment: Organizations increasingly pair information security management (ISO 27001) with AI-specific governance (ISO 42001) to create integrated controls that address both traditional and AI-introduced risks.

Sector-Specific and Regional Requirements

• DORA (EU 2022/2554): The Digital Operational Resilience Act requires financial services organizations to demonstrate preparedness for and resilience to digital disruptions, including AI-related incidents. Imposes specific incident reporting timelines.
• NIS2 (EU 2022/2555): Strengthens cybersecurity requirements across essential and important sectors. Mandates early warning within 24 hours of a significant incident and further follow-up reports. Adversarial-robustness testing for AI models is part of the broader EU AI Act obligations that intersect with NIS2 compliance.
• SEC Cyber-Incident Rule: Requires public companies to report material cybersecurity events within four business days of determining materiality. AI-related incidents that meet the materiality threshold fall under this disclosure obligation.
• U.S. Federal AI Policy (OMB M-25-21): Establishes governance expectations for federal AI projects aligned to the NIST AI RMF. Agencies must document AI use cases, conduct risk assessments, and implement safeguards proportionate to the system's impact level.

What The Regulatory Landscape Means for Organizations

Organizations must demonstrate that they know where AI is operating, how it is governed, what risks it introduces, and what controls are in place. Compliance is not optional, and enforcement timelines are accelerating, even as these frameworks evolve and new ones emerge. Organizations that build governance programs aligned to recognized frameworks (NIST AI RMF, ISO 42001) position themselves to meet multiple regulatory requirements simultaneously rather than responding to each mandate in isolation.

How Can Organizations Solve AI Security Challenges?

Establish AI Governance Programs

Organizations should start by building AI governance structures that set clear boundaries for AI use. This includes acceptable-use policies, intake processes for evaluating new AI tools, steering committees, and framework alignment with standards like NIST AI RMF and ISO 42001. Governance creates the foundation for consistent, accountable AI adoption across the enterprise.

Gain Visibility Through Discovery and Inventory

Before organizations can secure AI, they need to know where it exists. AI discovery identifies shadow AI tools, SaaS-embedded agents, AI coding environments, and autonomous systems operating across endpoints, networks, and cloud environments. Comprehensive inventory tracks AI tools, usage patterns, and data access across the complete IT estate.

Implement Identity and Access Controls for AI Agents

Traditional identity and access management must evolve to account for non-human identities, including agentic AI. Organizations build agent-native identity governance that defines what agents can access, establishes lifecycle management (including offboarding), and enforces behavioral boundaries. Kill switches and human-in-the-loop controls provide safeguards when autonomous systems make contextual decisions.

Secure the AI Application Lifecycle

Organizations must integrate security into every phase of AI application development. This includes threat modeling and architecture review, secure code practices for AI software development kits (SDKs) and prompt construction, adversarial testing against large language model (LLM)-specific attack classes, and supply chain security for third-party models and components. Security testing expands to include prompt injection, data leakage, and excessive agency risks.

Protect Data Across AI Workflows

Data security controls prevent sensitive, proprietary, or regulated information from leaking through prompts, model outputs, retrieval systems, or user interactions. Organizations adopting AI must implement guardrails, masking, tokenization, and audit logging. Data classification catalogs risk levels across models, training sets, and pipelines to ensure the right protections are applied to the right data.

Deploy AI to Strengthen Security Operations

AI can become a powerful defensive tool, when implemented securely and correctly. Organizations can use machine learning to correlate signals across endpoints, networks, cloud, and identity systems. AI can improve threat hunting, accelerate detection and response, prioritize alerts based on business risk, and automate repetitive tasks. Using AI in this way allows security teams to focus their time on high-value analysis and decision-making.

Build Defenses Against AI-Powered Threats

Advanced detection capabilities across all attack surfaces are needed to counter AI-enhanced threats. Behavioral analytics identify sophisticated attacks that bypass traditional signature-based defenses. Continuous monitoring tracks emerging AI threat vectors, and incident response playbooks are updated to address AI-specific attack scenarios, including compromised agents and AI-driven social engineering.

Continuously Validate and Optimize

AI security is not a one-time effort. Organizations should plan on conducting ongoing and automated risk assessments, red team exercises, and exposure validation. They need to monitor for model drift, track changes in the threat landscape, and optimize defenses against emerging AI-enabled attack vectors. Regular health checks also become vital to ensure controls remain effective as AI capabilities and threats evolve.

How Do You Start an AI Security Program?

Organizations can start an AI security program by assessing their current AI footprint, establishing governance, prioritizing risk, closing the most critical gaps and building toward continuous validation.

AI security can feel overwhelming given the breadth of risk, the speed of change, and the number of stakeholders involved. However, not everything needs to be solved at once. A practical path follows a logical progression:

• Assess the current AI footprint. Identify where AI tools, models, agents, and copilots exist across the environment. A comprehensive inventory ensures more robust security implementation. The inventory should include sanctioned tools, shadow AI, and SaaS-embedded capabilities that may have acquired AI features after initial procurement.
• Establish governance foundations. Define acceptable-use policies, create an intake process for evaluating new AI tools, and assign clear ownership for AI risk decisions. Aligning to a recognized framework (NIST AI RMF or ISO 42001) avoids building from scratch.
• Classify and prioritize risk. Map AI systems against data sensitivity, decision authority, autonomy level, and regulatory exposure. Not all AI use carries equal risk. With clarity around risk prioritization, security investments can focus where the impact of failure is highest.
• Close the most critical gaps first. Common early wins include implementing data-loss controls on generative AI interactions, establishing identity governance for non-human accounts, and conducting threat modeling on customer-facing AI applications.
• Build toward continuous validation. Like all security, AI security is not a project with a finish line. By establishing recurring assessments, red team exercises, and continuous monitoring, security can evolve alongside AI maturity and threat landscape shifts.

If the organization starts with visibility, moves to governance, then layers technical controls proportionate to risk, each step builds on the one before it, and progress compounds over time.

Glossary of AI Security Terms

The following table defines terms you'll encounter in artificial intelligence (AI) discussions. While not exhaustive, this list provides the foundational vocabulary you need to engage meaningfully in these conversations.

Term	Definition
Agentic AI	The newest frontier of AI that acts autonomously to complete complex, multi-step tasks. It can break down goals into steps, use tools, make decisions, and execute processes with minimal human supervision.
AI Agents	AI models designed to handle specific tasks. Multiple agents can work together dynamically within an Agentic AI system to achieve larger, more complex goals.
API Keys / Access Tokens	Unique codes that grant access to software systems or services. Must NEVER be entered into public AI tools.
Artificial Intelligence (AI)	A complex mathematical system that generates answers and performs tasks based on ingested data — including understanding language, recognizing patterns, and solving problems.
Artificial Narrow Intelligence (ANI)	AI systems designed to perform specific, singular tasks within a strictly defined scope.
Amazon Quick	Amazon Quick (also known as Amazon Quick Suite) is an AI-powered enterprise assistant and workspace from AWS. It unifies data discovery, deep research, business intelligence visualization, and task automation into a single platform using natural language.
ChatGPT	A publicly available generative AI tool developed by OpenAI. Data entered into ChatGPT may be used to train and improve its models, potentially making it accessible to other users.
Claude	A publicly available generative AI tool developed by Anthropic. Like other public AI tools, data entered may be used for model training.
Copilot	A generative AI tool developed by Microsoft, integrated into many Microsoft products. Powered by LLMs.
Context Window	Everything the AI model can see at once. Your prompt, the conversation history, any documents you've attached, system instructions. If it's in the context window, the model can use it.
Credentials	Usernames, passwords, or other authentication information. Must NEVER be entered into public AI tools.
Customer Data	Any information belonging to or identifying GuidePoint Security clients. Must NEVER be entered into public AI tools.
Deep Learning	ML subfield using multi-layered neural networks to learn and extract data features that enable computers to handle complex tasks.
Generative AI	A type of AI that creates entirely new content — including text, images, code, and presentations – based on patterns learned from data. Common tools include ChatGPT, Claude, and Copilot, all powered by LLMs.
Hallucination (AI)	When an AI model confidently produces false or fabricated information — such as fake statistics, wrong citations, or inaccurate technical details — especially when it lacks sufficient source data.
Harness	The software framework that wraps around an AI model to make it actually do something useful. The model on its own just predicts text; the harness is the surrounding "plumbing" that feeds it inputs, gives it tools to use, manages the context, and captures the results.
Jailbreak	Techniques used to bypass an AI model’s safety controls and restrictions
Large Language Models (LLMs)	The engine. A deep-learning algorithm trained on massive datasets of text designed to understand, summarize, translate, predict, and generate human language content. AI systems that read and respond in human language, allowing users to ask questions in plain text rather than code and receive meaningful responses. Examples: ChatGPT, Copilot, Claude, Gemini.
Machine Learning	AI subset; trains systems to learn from data and make decisions/predictions based on patterns without human programming
Model Context Protocol (MCP)	How agents connect to tools, data sources, and external systems.
Neural Networks	Type of ML algorithm that mimics the human brain structure and function, allows machines to learn & process complex data.
Non-human Identity (NHI)	The credentials and permissions assigned to AI agents in your systems. Every agent is a user. Most organizations don't manage them like users. That's the gap.
Prompt	The input or question a user types into an AI system to generate a response.
Prompt Injection	Manipulating an AI model through malicious instructions hidden in prompts or external content to make it ignore its original instructions.
Proprietary Information	Confidential internal data including pricing, methodologies, internal processes, and strategic plans. Must NEVER be entered into public AI tools.
Public AI Tools	AI platforms available to the general public (e.g., ChatGPT, Claude, Copilot). Data entered can be used for model training and may become accessible to other users.
Retrieval-Augmented Generation (RAG)	How AI accesses information beyond its training data. Think of it as the model looking something up before it answers.
Reasoning Models (OpenAI, o1/o3, DeepSeek R1)	A specialized LLM designed to break complex problems into smaller, sequential steps and evaluate them logically before answering.
Token	Tokens are the basic units of data that a language model processes. Instead of reading words or letters one by one, the AI breaks text into smaller chunks of language, usually a word, part of a word, a number, or symbol, that the model processes one piece at a time. Tokens impact how AI tools (e.g., ChatGPT, Claude, or Copilot)  function and how much they cost to use.
Training Data / Model Training	The process by which AI systems learn from input data. When users enter information into public AI tools, that data can be incorporated into the training pipeline and potentially surfaced to others.