The biggest AI security risks facing enterprises in 2026 are prompt injection attacks, data leakage through LLM outputs, shadow AI usage outside IT governance, and insecure integrations between AI applications and internal systems. Protecting against them requires treating LLM-powered applications as a new attack surface, not an extension of existing application security, with controls covering input validation, output handling, access scoping, and continuous monitoring of how employees are actually using AI across the organisation.
The sections below break down each risk in detail and what a practical security programme for AI adoption actually looks like in 2026.
Why AI Security Is a Distinct Discipline, Not Just Application Security?
Traditional application security assumes a relatively predictable system: defined inputs, defined logic, defined outputs. LLM-powered applications break that assumption in three important ways.
Inputs are natural language, not structured data: Traditional input validation looks for malformed data or injection patterns in known formats. An LLM accepts open-ended natural language, which means malicious instructions can be disguised as ordinary conversation, customer messages, or even content embedded in documents the AI is asked to summarise.
Outputs are probabilistic, not deterministic: The same input can produce different outputs across runs, and outputs can include information the model was never explicitly instructed to share, simply because it was present in its training data, its context window, or a connected data source.
The attack surface includes the model's behaviour itself: Beyond the surrounding application code, the model's own reasoning can be manipulated through carefully crafted prompts, a category of risk that did not meaningfully exist before LLMs became embedded in business applications.
Digisecuritas' AI Security practice treats these as first-class risks, assessing LLM-powered applications, integrations, and usage patterns with the same rigour applied to any other critical system in your environment.
The Top AI Security Risks Facing Enterprises in 2026
1. Prompt Injection Attacks
Prompt injection occurs when an attacker crafts input designed to override an LLM's intended instructions, causing it to ignore its original guardrails and follow the attacker's instructions instead.
Two variants matter most for enterprise applications:
- Direct prompt injection: An attacker types malicious instructions directly into a chat interface, attempting to extract system prompts, bypass content restrictions, or manipulate the model's behaviour
- Indirect prompt injection: Malicious instructions are embedded in content the LLM processes on the user's behalf, such as a webpage, email, or document, which the model treats as legitimate input it should follow
Indirect prompt injection is the more dangerous and harder to defend category, because the victim never sees the malicious instruction. It arrives hidden inside a file or page the AI was simply asked to read.
Mitigation approaches: Treat all content the LLM processes, not just direct user input, as untrusted. Implement strict separation between system instructions and user or retrieved content. Apply output filtering before any LLM response triggers a downstream action.
2. Data Leakage Through Model Outputs
LLM-powered applications can leak sensitive data in several distinct ways:
- Training data leakage: If a model was fine-tuned on internal data without proper safeguards, it may reproduce fragments of that data in unrelated responses
- Context window leakage: Information included in a model's context, such as system prompts, retrieved documents, or prior conversation history, can be exposed through carefully crafted queries
- Cross-user leakage: Poorly architected multi-tenant AI applications can occasionally surface one user's data in another user's session if context isolation is not properly enforced
For any enterprise handling regulated data, this risk intersects directly with compliance obligations. An LLM that inadvertently surfaces customer PII or protected health information in its output creates the same regulatory exposure as a traditional data breach.
Digisecuritas' Data Classification and Protection Strategy work extends naturally into AI governance, ensuring sensitive data is properly classified and access-controlled before it ever reaches an LLM-powered application.
3. Shadow AI
Shadow AI refers to employees using AI tools, often free consumer-grade chatbots, outside any sanctioned or governed process. It is the AI-era equivalent of shadow IT, and it is currently one of the largest and least visible enterprise risks.
The core problem: when an employee pastes a customer contract, proprietary code, or sensitive financial data into a consumer AI tool to get help drafting something faster, that data may be retained, used for model training, or processed in jurisdictions that violate the organisation's data handling obligations, all without IT or security ever knowing it happened.
Why this is hard to control: unlike shadow IT, which usually requires installing software or provisioning access, shadow AI requires nothing more than a browser and a few seconds. Blocking access to popular AI tools is rarely effective on its own and often pushes usage further underground.
A more effective approach combines clear AI usage policy, approved internal alternatives that are actually as convenient as the unsanctioned tools, and monitoring for sensitive data leaving the organisation through AI interfaces specifically.
4. Insecure Integrations and Excessive Agency
As LLMs move from simple chat interfaces toward agentic systems that can take actions, send emails, query databases, or call other systems, the risk profile changes substantially. An LLM with excessive permissions or poorly scoped access to internal systems can be manipulated into taking unintended, damaging actions, not just generating bad text output.
This is particularly relevant as enterprises connect LLMs to internal tools through frameworks such as the Model Context Protocol, where an AI agent may have standing access to email, calendars, file storage, or business systems.
Mitigation approaches: Apply the principle of least privilege to any AI agent's access, exactly as you would for a human user or a service account. Require human approval for consequential actions. Log and monitor every action an AI agent takes against internal systems, not just its conversational outputs.
Digisecuritas' Identity and Access Management (IAM) practice extends access governance principles to AI agents and integrations, ensuring AI systems operate under the same least-privilege controls applied across your environment.
5. Model and Supply Chain Risk
Most enterprises do not build their own foundation models. They consume them through APIs, open-source weights, or embedded vendor products, which introduces supply chain risk similar to any third-party software dependency, with an added layer of opacity around how the underlying model was trained and what data it may have been exposed to.
Key questions to ask of any AI vendor or model provider: What happens to data sent to the model? Is it used for further training? Where is it processed and stored, and does that satisfy your regulatory obligations? What security testing has the vendor's platform undergone?
Digisecuritas' Third-Party Risk Management Consulting framework applies directly to AI vendor evaluation, extending existing vendor risk processes to cover the specific data handling and model behaviour questions AI tools introduce.
Building an AI Security Programme: Where to Start
A practical AI security programme for 2026 covers four areas:
- Discovery and visibility: You cannot secure what you do not know exists. Identify every AI tool currently in use across the organisation, sanctioned and unsanctioned, before building policy around it.
- Policy and governance: Define clear, enforceable rules for what data can and cannot be processed by AI tools, which tools are approved, and what approval process exists for new AI adoption requests.
- Technical controls: Implement input and output filtering for sanctioned AI applications, least-privilege access for AI agents and integrations, and monitoring for sensitive data leaving the organisation through AI interfaces.
- Testing and validation: LLM-powered applications should undergo security testing specifically designed for AI systems, covering prompt injection resistance, data leakage scenarios, and excessive agency risks, not just standard application security testing.
This testing discipline is becoming as routine as application penetration testing was a decade ago. For organisations that already run regular penetration testing against their core applications, extending that same rigour to AI-powered features is a natural and increasingly necessary next step, particularly as testing approaches themselves evolve toward the continuous, automated models covered in our piece on agentic AI pen testing.
Conclusion
AI adoption inside enterprises is not slowing down, and the risks outlined here are not theoretical edge cases. They are already showing up in real environments, often discovered well after the exposure occurred rather than before.
The organisations managing this well are not the ones avoiding AI. They are the ones treating AI-powered applications as a genuine attack surface from day one, with the same discipline applied to discovery, governance, technical controls, and testing that mature security programmes already apply everywhere else.
Waiting until after an incident to build that discipline is the most expensive way to learn it is necessary.
Digisecuritas helps organisations assess and secure AI-powered applications, govern shadow AI risk, and build practical AI security programmes that keep pace with adoption.
Book a Discovery Call to discuss where your organisation's AI security gaps actually are.
Frequently Asked Questions
What is prompt injection and why does it matter for enterprises?
Prompt injection is an attack technique where malicious instructions are crafted to override an LLM's intended behaviour, either through direct input or hidden within content the model processes. It matters because it can cause AI applications to leak sensitive information, bypass safety restrictions, or take unintended actions.
What is shadow AI and how big a risk is it really?
Shadow AI refers to employees using AI tools outside any sanctioned governance process, often consumer-grade chatbots. It is a significant and growing risk because sensitive company or customer data can be exposed to third-party systems with no visibility, monitoring, or control from the organisation.
Can LLM-powered applications be security tested like traditional applications?
Yes, though the testing approach differs. AI-specific security testing covers prompt injection resistance, data leakage scenarios, and excessive agency risks in addition to standard application security testing, since traditional test cases do not cover the unique risks LLMs introduce.
Does using a reputable AI vendor eliminate these risks?
No. Vendor platform security and your organisation's own configuration, integration, and access governance are separate concerns. A secure underlying model does not prevent data leakage caused by how your organisation has integrated and granted access to that model.
What is the first step an organisation should take to improve AI security?
Start with discovery: identify every AI tool currently in use across the organisation, sanctioned and unsanctioned. Most organisations are surprised by how much AI usage already exists outside formal visibility, and that visibility gap is the foundation every other control depends on.