Many teams know about threats hidden behind AI implementation, but do nothing about them. The AI at Work report highlights that 42% of organizations have detected risks but left them unaddressed. 10% have no visibility into AI tool usage.
Without taking care of enterprise AI security, you create a large attack surface that makes your data vulnerable to prompt injection. Poor guardrails and shadow AI also increase the risks of data exposure and data leakage.
However, these and other enterprise AI security issues are preventable. You just need to create a strong security strategy, covering the multiple layers of enterprise IT systems. Our AI software developers' insights and an AI security checklist shared below should help with that.
What does enterprise AI security mean?
Enterprise AI security is a combination of practices, technologies, and policies an organization follows to keep AI systems secure across their full operating chain. It includes model endpoints, agent logic, retrieval layers, tools, identities, and data.
While traditional cybersecurity covers networks, computers, applications, users, and databases, enterprise AI security solutions include AI-specific guardrails on top of these standard approaches.
Core enterprise AI security components and ways to cover them
Enterprise AI security requires a bottom-up approach that addresses vulnerabilities across every system layer, including:
Identity and access
AI agents and service accounts must have separate, scoped non-human identities and SSO/MFA identification for proper access control / IAM. It's also necessary to configure RBAC and least-privilege tool access, limiting what actions AI agents and services are allowed to take.
Data and regulatory compliance
Organizations need enterprise security products and workflows to classify data into public, internal, confidential, and restricted. Classification must happen before data ends up in an AI system. You also need to sign DPA contracts with third-party providers handling your data to protect privacy and stay compliant.
Integration and connectivity
Since enterprise AI systems connect with a wide variety of tools and services, you need to secure the API/tool layer. Start with auditing what AI can connect to (including owner, purpose, permissions, data accessed, provider, and risk level) and then personalize access for every user, agent, connector, and service. Avoid blanket access and specify failover behavior when the system is not available.
Prompt and input security
Enterprises need AI security tools to protect systems from unwanted malicious, manipulated, or sensitive information. You should also run real model testing to see how the guardrails work and check the most common injection scenarios.
Output quality and governance
Every output must be traceable to a specific source so you can rely on it. Proper AI systems provide cited answers and automatically trigger escalation when they cannot respond. High-risk actions or decisions like sending, deleting, or paying still require manual checks.
Besides the listed components, enterprise AI security also covers system monitoring, detection, and incident response. You need to implement AI activity logging, behavioral anomaly detection, audit trails, security alerts, and reliable investigation mechanisms.
Enterprise AI security checklist to go through before deployment
If you want a quick way to assess whether your organization is ready for secure AI adoption or to audit your existing approaches, we've put together a checklist. Go through the core enterprise security components and tick the relevant ones.
Identity & access
- SSO configured to your corporate identity provider — no separate credentials required
- MFA for all human user sessions
- API service accounts use scoped, rotated credentials instead of interactive login
- No role or service account has broader access than the job requires
- Session timeout configured for both web and API sessions within your policy window
Data, storage & compliance
- Every data source accessed by AI is classified, with access limited to the use case
- Data residency confirmed in writing and storage location meets jurisdictional requirements
- Encryption in transit (TLS 1.2+) and at rest (AES-256) verified
- Model training on our data disabled by default and confirmed contractually
- Compliance with an AI risk management framework (NIST / ISO 42001)
- Compliance (GDPR / HIPAA / CCPA) mapping complete and confirmed by legal
Integration & connectivity
- API credentials scoped to only the objects required
- Any data pushed to external systems is sent over protected, access-controlled connections
- API keys and credentials excluded from prompts, outputs, logs, and training data
- Failover behavior defined for cases when a connected system is unavailable
Prompt and input security
- All external inputs (documents, APIs, retrieved context) are treated as untrusted
- Prompt injection, indirect injection, and context data poisoning scenarios have been tested
- System prompts are versioned and reviewed like source code
Output quality and governance
- Every output is traceable to a specific source document
- When the tool lacks a reliable source, it flags uncertainty or escalates
- AI outputs are logged with timestamps, user attribution, and the related query
- Model outputs are constrained before being passed to downstream logic or tools
- The model version in use is recorded, and there is a process to re-validate outputs after model updates
Note. Even if you don't score 100%, the system may be secure enough for the use case. So when assessing a system, you need to understand related risks and their potential impact on the business process and user security.
Key threats to enterprise AI security to manage
Based on Deloitte's report on The State of AI in the Enterprise, data privacy and security tops the list of worries, mentioned by 73% of respondents. While more organizations use AI for automation, they still don't feel safe implementing it.
When creating a strategy for enterprise AI security, you must take into account the unique threats related to AI adoption. They largely fall into the following categories:
- Input-layer threats: direct prompt injection, indirect context poisoning, jailbreaking, token flooding.
- Identity and access threats: agent over-permissioning, credential exposure, credential rotation failures.
- Data-related threats and Shadow AI: data exposure, unsanctioned tools with confidential data sharing, unauthorized model training on your data.
- Model and output threats: hallucinations, unverified output, lack of human supervision, model theft or extraction.
- Supply chain security and integration threats: MCP server weaknesses, unvetted plugin or model use, AI-generated code vulnerabilities.
- Agent-specific threats: AI-to-AI attacks, excessive AI agency, loss of traceability, model drift.
The AI security checklist shared above covers most of these threats. However, you still need to audit your current systems to know which security measures are most crucial and prioritize them accordingly. Based on our experience, we also recommend designing a security framework before you adopt AI systems since retrofitting governance is more difficult and resource-intensive.
It's also important to train teams on secure data handling and assign clear ownership for AI workflows. When we asked tech teams what the biggest barriers to AI automation are that they wish to be removed, one of them was:
"The barrier is in consideration of security, actual access and permissions to create agents and workflows. To a certain extent, it is also the confidence and capability of the teams."
— Kaylee, Cyber Security Lead
The task of an IT team is to build a system that deserves trust and to minimize risks. Find more practical tips on safe and reliable AI deployment in our guide.
A practical approach to enterprise AI security
If, after going through the enterprise AI security checklist, you find out that your system is not as secure as expected, here's a standard path to follow:
Audit your systems and classify data
Understand your weaknesses and potential threats. Then, prioritize threats based on their impact and likelihood to address the most critical ones first. Your goal is not to create a perfect model, but to minimize risks and define safe AI use cases.
Make sure every system has its identity and suitable controls
Every AI component must have clear responsibility and constraints, just like human employees. Prefer systems with customizable access and document what each tool and agent does.
We asked Yaroslav Stus, our Associate Director of Engineering, about how they do it while developing Leebry:
"We treat the content in Confluence and Google Drive as information that is generally available to everyone in the company. The way a public library works. If something needs to be restricted, the controls live in the source system, not in the AI layer.
For per-user systems, where access is the entire point of how the system works. Jira is the clearest example. We are not indexing Jira data. We are querying it in the user's context every time. The audit log stays in the destination. You can see the information on who made the change, left the comment, and created the ticket.
The principle behind both: the AI tool should not invent its own access model. It either treats a source as shared, or it inherits the per-user rules already set in the source system."
— Yaroslav Stus, Associate Director of Engineering @ MacPaw
You can find more engineering insights from our Work AI creation process in our newsletter.
Protect data throughout its lifecycle
Look at what and how data flows within your AI system and strengthen security through more control. You need to encrypt data in transit and at rest, minimize models' access to confidential information, and log access to sensitive data. It's also necessary to prevent secrets, API keys, and credentials from ending up in prompts and training data to avoid leakage and injection.
Configure continuous monitoring and supervision
Combine automated adversarial tests in CI/CD with periodic human checks to test AI for the most common threats. This will show you what to improve and help troubleshoot when the system gets compromised.
You also need to monitor how AI actually behaves. Ask controversial questions to check for sensitive data access. Monitor sudden changes in model behavior and unusual actions. Finally, establish a clear reporting flow for teams using AI to know when something goes wrong.
Overall, enterprise AI security is a part of overall AI governance within an organization. That's why building reliable systems requires a more holistic approach than eliminating security threats. You must also make AI transparent, explainable, and unbiased to deserve user trust and achieve a decent level of automation. Our AI at Work report shows that only 2% of organizations trust AI without human review, which means lower efficiency and resistance to using it.
Summarizing enterprise AI security
Enterprise-level security for AI is not a static concept. The measures you need to take depend on how you use AI and related risks. Higher-risk systems that affect critical workflows and may cause more harm require more supervision and stronger limitations. More standard workflows, like knowledge management or L1 support, are safer to automate with the right access controls and escalation paths.
FAQs
How are enterprises handling security with AI agents?
Enterprises can achieve AI agent security by managing every autonomous agent as a separate digital identity. You need to treat agents as digital workers by setting clear access boundaries, short-lived credentials, intermediary gateways, and activity tracking. This way, AI agents receive access only to the information required for their direct tasks.
How does AI security differ from traditional IT security?
AI security covers the AI-specific risks that are broader than traditional IT security. While IT security covers deterministic infrastructure, networks, and data-at-rest, AI security governs probabilistic, data-driven systems, including training data, the model itself, and the inference process.
How to understand whether enterprise AI systems are secure?
To estimate the security of an enterprise AI system, you need to check it across the entire lifecycle. The security audit must include multiple layers of AI systems, including data processing and access, model operations, and connected agents and tools.
What are the main risks to enterprise AI security?
The main risks to enterprise AI security are typically prompt injection, sensitive data leakage, overprovisioning permissions, and data poisoning through malicious or misleading data. However, the actual threats depend on the type of system you use within your enterprise and what security measures are currently available or still missing.
