This is the Trace Id: c1d2c4c81bfce7e375816b7f27107ead

Agents built into your everyday workflow. Read the announcement

AI Application Security Series 1: Security considerations when adopting AI tools

Share
A woman looking at a computer screen.

AI is at the center of work and risk

Artificial intelligence (AI) has rapidly moved from experimentation to execution, reshaping how organizations operate, make decisions, and manage risk. As AI becomes embedded in productivity, collaboration, and security workflows, it is transforming both the speed and scale at which work gets done.

But this acceleration comes with additional pathways for exploitation. Threat actors are already leveraging AI for reconnaissance, social engineering, and automation; turning a powerful defensive tool into a potential offensive weapon. Security, therefore, is no longer about protecting traditional systems and data; it’s about understanding and securing how AI is accessed, applied, and implemented within your organization.

AI tools also introduce subtler challenges as well. Systems can misinterpret data, make errors, or exhibit unexpected preferences. Outputs can change over time as models are updated or retrained. This means security isn’t just about protecting the software; it’s about monitoring behaviour, validating results, and implementing governance frameworks that ensure AI tools are used responsibly. Without these measures, even well-intentioned applications can introduce operational risks.

This article explores practical security considerations organisations should keep in mind as AI becomes central to work. From helping prevent misuse and enforcing governance to monitoring and controlling access, we’ll look at how organizations can benefit from AI safely and effectively.

Expanding pathways for exploiting AI applications

The growing reliance on AI introduces more potential entry paths for threat actors to exploit. Threat actors can now leverage AI can help scale phishing campaigns, tailor social engineering messages, or automate repetitive attack tasks.

As AI tools become integral to business operations, organizations must recognise that adoption broadens the attack surface and amplifies existing risks. Four key areas of concern include:

Data risk: With the right permissions, Microsoft Copilot can access your emails, documents, and Teams messages to deliver context-aware responses. This access can inadvertently expose sensitive information. For example, if a user includes sensitive information in a prompt such as passwords, proprietary code, or confidential data, it could be exposed. Organizations can mitigate this risk with data loss prevention (DLP) policies in Microsoft Purview, least-privilege access, and enforced data classification.

AI behaviour and content risk: Copilot responds directly to the inputs it receives. Malicious or misleading instructions—whether embedded or hidden in documents or knowledge bases—can produce unsafe outputs. Prompt injection attacks or corrupted content could reveal sensitive information or produce incorrect recommendations. External adversaries aren't the only risk, employees with legitimate access can also misuse systems. Organizations can reduce risk using input validation, insider risk monitoring, metadata scanning, controlled content ingestion, and human review.

Automation and operational misuse: AI performs repetitive tasks quickly, from drafting emails to generating reports. This efficiency can amplify errors or be misused by insiders. For example, a security analyst under pressure could over-rely on Security Copilot outputs and miss crucial context. Combining human review, monitoring, and governance policies such as compulsory employee training ensures AI assists rather than replaces human judgement.

Deployment and infrastructure risk: Copilot integrates deeply with Microsoft 365, Azure, and third-party applications. Misconfigured connectors or overly permissive integrations can expand access, creating potential entry points for attackers. Regular configuration reviews, conditional access policies, and API monitoring help maintain a secure environment.

AI incidents in the wild: Lessons from real attacks

As AI tools like Microsoft Copilot become increasingly central to enterprise operations, research and real-world testing are uncovering new vulnerabilities. Threat actors are quick to explore additional ways to exploit these tools such as using them as part of their attack chain like living-off-the-land technique in traditional attacks. Even though this technology is new, Microsoft is integrating it into known and proven processes such as coordinated vulnerability disclosure to ensure that risks are safely identified and mitigated. These cases provide valuable lessons for organizations, not because the tools “failed” in a conventional sense, but because they reveal unexpected ways AI can be misused in its interactions with data and human workflows.

CVE-2025-32711 (2025)
EchoLeak – now fixed – was a technique for a multi-stage cross-prompt injection attack that in certain conditions can exfiltrate limited data to which the victim already has access. A carefully crafted email—designed to look benign—could silently “poison” the prompt Copilot sees, causing it to leak limited internal data without knowledge of the victim. Microsoft quickly released updates to address the flaw CVE-2025-32711. The vulnerability highlighted a subtle but serious risk: the potential for AI systems to unintentionally expose sensitive customer data through indirect prompt manipulation.

Over-permission risks
Copilot inherits the permissions of its users. If someone retains access to data from a previous department, Copilot can surface that information in generated outputs. Similarly, sensitive data available to one employee might appear in AI summaries, creating compliance and privacy risks. Organizations can mitigate this with least-privilege access, role-based controls, and DLP policies in Microsoft Purview. In addition, using M365 Copilot’s Researcher mode can help proactively identify permissioning leaks before they become a security issue. The key principle is to trust AI only with data you are comfortable it accesses—essentially, information you could afford to expose. Organizations should implement a risk model assessment and mitigation plan to carefully evaluate what data Copilot can safely interact with, rather than placing it at the center of highly confidential workflows.

Insider misuse and operational errors
Not all incidents come from external threats. In some cases, internal users may use tools like copilot to generate reports which contain sensitive data without reviewing them thoroughly. Mitigation includes training, monitoring, anomaly detection, and governance policies to keep AI a trusted assistant rather than an unchecked tool.

Indirect data poisoning and prompt manipulation 
AI systems are vulnerable in many ways, including through subtle yet powerful techniques like indirect data poisoning and prompt manipulation, which can quietly shape a model’s behavior without obvious warning signs. These methods involve crafting inputs that steer outputs in risky ways such as to expose sensitive information or introducing bias. Hidden prompts such as text encoded in hexadecimal or invisible formatting can subtly influence Copilot’s summaries, occasionally surfacing outdated or unexpected information. By applying controlled content ingestion, validating metadata, and maintaining thoughtful oversight of AI interactions, organizations can keep AI tools reliable and ensure they continue to add value without introducing unintended risks.

These examples above show that AI security is often about human behaviour, data handling, and interpretation, not just software flaws. With proper safeguards, organizations can leverage Copilot tools effectively while minimizing unintended exposure. Learn more at Secure AI For a Strong Foundation - Training | Microsoft Learn.

Incident readiness in the age of AI: A prevention–detection–response mapping


The path forward isn’t about eliminating every possible threat—that’s not realistic. It’s about designing with security in mind from day one. That means applying a Zero Trust mindset. The organizations that will be most resilient are the ones that treat those incidents as moments to learn and adapt, feeding every lesson back into their security frameworks.

Below is a mapping of practical threat scenarios to preventive controls, detection signals, and response playbooks that organizations can use to build resilience from the start.
  • Prevention: Apply Purview sensitivity labels and DLP rules; block Copilot from processing “Highly Confidential” files.

    Detection:     Purview/UAL logs showing Copilot queries against restricted files; Sentinel DLP alerts.

    Response:     Quarantine output, notify file owner, adjust DLP policy.

    Practical scenario:     A finance director asks Copilot to “summarize all quarterly reports.” Sensitive board documents are blocked from inclusion.
  • Prevention: Enable Prompt Shields; sanitize inputs from emails, logs, and external feeds before Copilot processes them.

    Detection:     Sentinel alerts on suspicious or jailbreak-style user input logged by the chatbot.

    Response:     Block malicious session, remove unsafe outputs, tighten guardrail.

    Practical scenario:     A threat actor sends a crafted prompt to manipulate the organizations AI-powered customer support chatbot; the chatbot ignores the injected instructions.
  • Prevention: Validate ingestion sources, enforce source signing, and limit write access.

    Detection: Audit logs and canary doc access alerts; Sentinel detects unusual Knowledge Base updates.

    Response:     Roll back poisoned Knowledge Base, revoke ingestion keys, alert incident response.

    Practical scenario:     A rogue insider uploads a fake “incident response” playbook; canary doc triggers detection.
  • Prevention: Validate ingestion sources, enforce source signing, and limit write access.

    Detection:     Audit logs and canary doc access alerts; Sentinel detects unusual Knowledge Base updates.

    Response:     Rotate exposed credentials, remove unsafe commit, retrain team.

    Practical scenario:     A junior dev pastes an API key in code; Copilot suggests it elsewhere, but secret scanning stops the merge.
  • Prevention: Role-based access control and least privilege for connectors; restrict access to sensitive systems.

    Detection:     Sentinel anomaly detection on bulk data queries from Copilot.

    Response:     Disable connector, suspend account, run HR/security review.

    Practical scenario:     A sales user extracts thousands of customer records via Copilot; anomalous query volume raises an alert.
  • Prevention: Restrict Copilot connector permissions; monitor cross-tenant or external data movement.

    Detection: Purview and Sentinel alerts for large file transfers or unusual connector use.

    Response: Block connector, revoke tokens, quarantine outputs.

    Practical scenario: Copilot connected to OneDrive attempts to send labelled documents to a personal Gmail connector.
  • Prevention: Apply Conditional Access and Defender for Cloud Apps to block unsanctioned AI services.

    Detection: Microsoft Cloud App Security/Defender alerts on AI app traffic; Sentinel analytics on OAuth grant events.

    Response:     Revoke tokens, notify user, enforce sanctioned Copilot use.

    Practical scenario:     An employee tries connecting Copilot to an external AI summarization tool without approval.
  • Prevention: Configure Safe Links/Safe Attachments, train staff on Copilot-assisted phishing risks.

    Detection:     Defender detects unusual mail-sending spikes or Copilot-authored phrasing patterns.

    Response:     Block sender, revoke mailbox tokens, auto-disable rules.

    Practical scenario:     A compromised account uses M365 Copilot to generate spear-phishing campaigns.
  • Prevention: Train staff on Copilot limitations; enforce human-in-the-loop review for sensitive workflows.

    Detection:     Sentinel detects high-risk actions triggered by AI-suggested queries.

    Response:     Roll back action, notify approver, retrain user.

    Practical scenario:     Copilot suggests the wrong remediation command in a security operation center (SOC) alert; analyst review prevents damage.

Building secure AI from the start

AI copilots are redefining how organizations operate—helping to accelerate insight, reduce manual effort, and enable teams to focus on higher-value work. But progress without protection is short-lived. The real competitive advantage lies not in adopting AI the fastest, but in adopting it securely and responsibly from day one.

Microsoft’s Copilot ecosystem, including Security Copilot, demonstrates what this balance looks like in action: innovation guided by governance. When organizations implement strong access controls, robust data-handling policies, and continuous oversight of how copilots are used, they transform AI from a productivity enhancer into a trusted strategic asset.

Building secure AI from the start isn’t just a technical priority, it’s a leadership imperative. Executives who embed security, readiness, and transparency into their AI programs will not only reduce risk but also strengthen stakeholder confidence, regulatory trust, and long-term resilience. In this model, Security Copilot becomes more than a tool, it becomes a partner in building a secure, accountable future for the enterprise.

More like this

Cover of a book showing a person using a laptop with a finger pointing at the keyboard.

Getting started with AI applications

Read the guide
A cover of a book showing a man looking at a computer screen.

Strategies for governing AI

Read the guide
A white line drawing of a paper in an envelope with the word New on a blue background.

Get the CISO Digest

Stay ahead with expert insights, industry trends, and security research in this bimonthly email series.
Sign up

Follow Microsoft Security