What Is AI Security?

AI security is the practice of both protecting AI systems from attack and using AI to strengthen an organisation’s defences. It covers risks like model manipulation, sensitive data exposure, and AI-enabled attacks that target or exploit AI systems. For today’s security leaders, both dimensions carry serious organisational risk.

AI systems have advanced far beyond the pilot phase today. The majority of hiring processes, fraud detection, customer service experiences, and supply chain management rely on AI systems. If you were able to manipulate the integrity of one of these models, it could have a catastrophic ripple effect throughout the business. It could also lead to issues with your revenue, reputation, and compliance.

Data exposure risks are quantifiable. According to recent reports, nearly 40% of all enterprise AI interactions use sensitive information. This occurs outside IT’s view, through unapproved personal accounts and unauthorised tools that security teams will never approve.

In essence, security teams face two conflicting realities. AI-based defences allow for quicker and more scalable identification of potential threats than what’s possible by manually analysing them. Adversaries are also using AI to automate phishing attacks, execute data poisoning, and create increasingly sophisticated attacks that are hard to identify.

For CISOs and compliance executives, the governance gap is another red flag. In fact, reports indicate that just 7% of companies have a dedicated AI governance team, and only 11% feel prepared to meet emerging regulatory requirements. Both a defensible architecture and a compliant posture support the use of AI security; however, most organisations require improvement in both areas.

AI security works on two fronts. Safeguarding AI systems and leveraging AI to strengthen cybersecurity starts with understanding attack vulnerability and where AI can be used to improve and strengthen their defences.

Securing AI Systems

Every AI system has three components that need protection: the model, the training data, and the inference pipeline—the same way you’d protect any critical IT infrastructure. Attacks like data poisoning, adversarial inputs, prompt injection, and model manipulation can quietly corrupt an AI’s output—or hand attackers access to sensitive data—long before anyone notices. For security architects, that means AI can’t be treated as an afterthought. It belongs in the existing security architecture as a first-tier threat.

Using AI to Improve Cybersecurity

AI also adds value by providing stronger detection and response capabilities. Security professionals use AI to identify anomalies at machine speeds, enhance threat intelligence analysis, and automate investigative workflows that would take analysts hours to perform manually. For SOC teams, the most immediate payoff of AI in cybersecurity is faster signal-to- action with much less manual effort.

The introduction of new attack surfaces into existing AI systems far exceeds the capabilities of current-generation security tools to protect against them. Security professionals are actively working to understand and counter these threats before they cause damage.

Data Poisoning

Data poisoning happens when attackers alter training data to affect how an AI model performs. The model learns what the attacker wants it to learn. For AI engineers, this makes data integrity in training a baseline security requirement rather than a post-deployment concern.

Adversarial Attacks

Adversarial attacks manipulate AI systems by crafting inputs that induce false outputs. For example, by adding a few stickers to an image of a stop sign, an AI system with computer vision could incorrectly identify it as something else. Likewise, an AI system using Natural Language Processing (NLP) may misinterpret a manipulated string of text as valid, leading to downstream problems. Adversarial testing is one of the most effective methods for security teams to discover vulnerabilities in their systems before attackers do.

Model Theft and Reverse Engineering

Proprietary AI models are significant investments of both time and money. By repeatedly querying an AI model, attackers can essentially reverse-engineer it and recreate the proprietary model that took years to develop. To combat these threats, CISOs need to recognise that protecting AI-based assets should require the same strategic effort as protecting source code or trade secrets.

Prompt Injection and Manipulation

Prompt injection is a growing threat to enterprise AI systems, in which an attacker inserts instructions into a user prompt to modify the AI system’s behaviour. To mitigate the risk of prompt injection, security architects developing on top of Large Language Models (LLMs) must include input validation and system-level guardrails as foundational controls, not optional add-ons.

AI security should not be treated as a replacement for traditional cybersecurity, but rather, an extension of it. The distinction matters for CISOs and leadership teams: while traditional and AI security share some common ground, they differ dramatically in scope, nature of threats, and controls needed to mitigate those risks effectively.

Traditional cybersecurity was designed for a world of known threats and static environments. AI security is based on a completely different paradigm: systems that can learn, adapt, and make decisions, and can be quietly compromised at either the data or model level long before a security alert is generated.

AI is no longer confined to a single team or use case. AI-powered systems are built into workflows that handle sensitive data, make important decisions, and interact with customers every day across the business. That footprint means that, for security teams, each of these systems must follow the same monitoring and governance rules as any other piece of critical infrastructure.

• Fraud detection systems analyse transaction patterns in real time to identify unusual ones. However, if the training data is corrupted or the models are changed, fraud can go unnoticed or legitimate transactions can be blocked on a large scale.
• AI-driven threat analysis helps security teams handle large volumes of alerts and telemetry faster than any analyst could by hand, surfacing the signals that really need to be investigated.
• Automated customer service agents handle sensitive account information, making them easy targets for quick injection attacks that steal personal information or bypass verification steps.
• AI copilots used by employees can accidentally share private information when they paste proprietary information into prompts or when the tool pulls from sources it was not meant to access.
• AI-assisted code generation tools make developers’ work move faster, but they can also create code patterns that are easy to hack if the model was trained on insecure sources or inputs that were changed.
• Predictive analytics platforms help people decide who to hire, who to lend money to, and how to use resources. If a model is compromised, it doesn’t just cause a security incident; it also makes the company responsible.
• AI-powered email and communication filtering keeps phishing and social engineering at bay. But it’s also a high-value target for attackers who want to blind an organisation’s first line of defence.

As AI systems become more critical to the enterprise’s operations, there’s growing pressure on compliance professionals to demonstrate that they are used responsibly (i.e., with documented oversight and controls in place). For executive leadership, responsible governance is what creates and maintains trust with customers, regulators, and boards over the long term.

Model Transparency

Traceability means that all stakeholders can understand the process by which an AI system arrived at its output. If a model is involved in making a hiring decision, determining fraud, or sending a security alert, all stakeholders should be able to audit the model and understand why it made the specific decision(s). Without transparency and traceability, it becomes extremely difficult to create a chain of accountability.

Auditability

Auditability requires that all AI systems maintain a record of their decisions and actions, as well as input data and model behaviour, throughout their operation. This provides a basis for incident response and regulatory examination, as well as a mechanism to identify areas for improvement based upon internal review. Both security and compliance rely heavily on the ability to audit AI system decisions.

Compliance with AI Regulations

The regulatory environment surrounding AI is rapidly evolving. The EU AI Act is the first comprehensive AI regulation worldwide, requiring full enforcement of high-risk AI systems by August 2026. Fines for non-compliance may exceed €35 million or 7% of global annual revenue. Although there is no U.S. equivalent to the EU AI Act, the NIST AI Risk Management Framework is a voluntary framework widely recognised and used by many organisations in the U.S. and is becoming an industry standard for managing AI risk.

Risk Management Frameworks

A successful risk management strategy for AI addresses all potential risks associated with the development, use, and operation of AI models across the entire model life cycle. This includes, but is not limited to, data collection, model development and deployment, and ongoing monitoring. The NIST’s AI Risk Management Framework identifies four primary functions: Govern, Map, Measure, and Manage. Incorporating these functions into normal business processes operationalises AI risk management, rather than leaving it aspirational.

Access Controls and Model Oversight

Governance is not just about ensuring that AI systems are operating correctly; it’s also about who can interact with those systems and under what circumstances. Ensuring proper access control, including role-based access, usage logs, and clearly defined thresholds for when human review is required for high-stakes decisions related to AI systems, is essential to the integrity of AI-driven output and to providing legal protection for the organisation using such systems.

Ethical AI and Bias Management

Just like any other type of model, AI models can inherit and amplify any bias present in the training dataset. Governance frameworks need to ensure mechanisms to regularly evaluate bias in the AI model and to conduct regular diversity reviews of the datasets used to train it. Additionally, AI governance frameworks should include clearly defined escalation procedures when an AI model generates results that lead to discrimination or produce unintended outcomes.

The threat landscape around AI is changing faster than most security programmes can handle. To keep up, CISOs need more than just knowledge; they need a proactive plan that takes into account how both attack methods and defensive capabilities are changing at the same time.

AI-powered phishing and social engineering have grown so much that they are no longer like the clumsy, generic campaigns of the past. In 2025, AI-powered phishing attacks occurred every 19 seconds, more than twice as often as in 2024. Attackers use AI to make messages more personal on a large scale. They use data from social media, professional profiles, and behaviour to make content that breaches filters as well as human judgement.

Adversarial machine learning has gone from an academic research topic to a real threat to businesses. The market for adversarial ML is expected to grow from $1.64 billion in 2025 to $5.67 billion by 2030. That growth reflects rising demand for tools that harden AI models and platforms built for adversarial testing. Companies are spending money on threat simulation services designed to put AI systems through their paces before hackers can get to them.

As businesses look for organised ways to manage AI deployments, security frameworks for AI are becoming more popular. Security teams use the OWASP Top 10 for Agentic Applications and the NIST AI Risk Management Framework as guides when building controls for AI systems. As regulatory pressure grows, more people are adopting these frameworks.

One of the most important issues in enterprise security right now is how to keep AI agents and autonomous systems safe. A 2026 Dark Reading poll found that 48% of cybersecurity experts believe agentic AI is the most dangerous attack vector this year. Autonomous agents have elevated permissions across many systems, and a misconfigured agent can expose data, grant itself additional privileges, or cause failures to spread throughout an environment.

As more businesses use third-party AI models, open-source components, and pre-trained systems, AI supply chain attacks are becoming a bigger problem. A compromised model in the supply chain can introduce weaknesses that are very hard to find once it’s in use. Security teams are starting to apply software supply chain practices, such as tracking software sources and verifying their integrity, directly to AI model pipelines.

Fraud and identity theft using deepfakes are making social engineering much more than just email. AI-generated audio and video can convincingly impersonate executives, vendors, and coworkers, leading to fake transactions or the theft of sensitive credentials. Deepfake detection tools are improving, but the gap between how convincingly they can be created and how reliably they can be spotted remains significant.