Article Summary: AI note takers join your meetings, transcribe everything said and save the recording and summary to the vendor's servers. Who can see that recording depends on the tool. Some keep your data inside your own Microsoft or Google environment and never use it for training while others store it on their own servers and may use it to improve their AI. Some also auto-join meetings from your calendar without anyone pressing record. Before you let one into a client or staff meeting, it is worth knowing where the recording goes and getting everyone's consent.
AI note takers have become normal in a short time.
You start a Teams, Zoom or Google Meet call and a bot joins to record the conversation. Minutes later everyone gets a tidy summary with action items.
It saves real time which is why staff often adopt these tools on their own before anyone has asked where the recording ends up.
The problem is that every word of the meeting (including the parts you would never put in writing) gets captured, stored somewhere and read by whoever has access. Few business owners have stopped to ask who that includes or what happens to the recording afterward.
What an AI Note Taker Actually Does
An AI note taker is a tool that joins a meeting, records the audio and sometimes the video, turns the speech into a written transcript and produces a summary. Common ones include Microsoft 365 Copilot in Teams, Otter, Fireflies and Fathom.
Most connect to your calendar so they can join automatically and some will sit in on any meeting on your schedule unless you turn that setting off.
The recording and transcript do not disappear when the call ends.
They are saved (usually in the cloud) where they can be searched, shared and exported later.
Where they are saved and who can reach them depends on which tool you use.
Who can see the recording?
Start with the obvious group which is anyone the meeting organizer shares the summary with.
Many note takers email the transcript to every attendee by default and some send it to people who were invited but never joined. When the meeting covered a sensitive topic, that distribution list matters.
Then there is the tool's own access.
With a cloud note-taker, the recording sits on the vendor's servers which means the vendor's systems (and in some cases its staff) can reach it under the terms you agreed to.
If the tool auto-joined from someone's calendar, the recording may live on an account you do not control that belongs to whichever employee connected the bot.
A law firm publication on the legal risks of AI note takers warned that letting a note taker vendor access or use your transcripts for its own purposes can even risk waiving attorney-client privilege for businesses that handle legal matters.
Does the tool use your meetings to train its AI?
This is where tools differ the most and it is worth checking before you choose one.
Microsoft states that Copilot in Teams does not use your prompts, responses or meeting content to train its AI models and that the data stays inside your organization's Microsoft 365 environment.
Microsoft's privacy documentation says this directly and notes the content is processed within the Microsoft 365 service boundary rather than on the public version of the AI.
Third-party note takers vary widely.
Some store your recordings on their own servers and (depending on the terms you accept) may use that data to improve their models.
Others say they do not train on customer data at all. The only way to know is to read the specific tool's privacy terms because two tools that look almost identical can treat your data very differently.
The Consent Question
Recording a meeting is not always yours to decide alone and the rules change depending on where you and the other people are.
In around a dozen U.S. states and in most Australian states, everyone in a conversation needs to agree to being recorded.
Federal U.S. law, most other states and the UK allow recording when one participant consents.
On top of that, the UK and Europe treat recording people as handling their personal data so under GDPR you generally have to tell participants you are recording, explain why and have a proper reason for doing it.
That is why the safest way to go about this is to tell people the meeting is being recorded, explain why and give them a chance to object before the bot starts.
For client meetings, HR conversations and anything covered by confidentiality, that matters even more and in some cases you should check with a lawyer before recording at all.
How to Use AI Note Takers Safely
You don't need to ban these tools to use them responsibly.
Do this instead:
- Pick an approved tool and say so. Decide which note taker your business uses and ask staff not to connect others to company meetings. This keeps your recordings in one place you control.
- Turn off auto-join. Set the tool to join only when someone chooses to record rather than automatically for every meeting on a calendar.
- Announce recording and get consent. Make it normal to say a meeting is being recorded at the start and to skip recording when someone objects.
- Prefer tools that keep data in your environment. A note taker that stores recordings inside your own Microsoft or Google tenant and does not train on your data is easier to control than one that holds everything on its own servers.
- Control who gets the summary. Check the default sharing setting so transcripts are not emailed to everyone including people who missed the meeting.
- Keep bots out of sensitive meetings. For legal, HR, financial and confidential client conversations, the default should be no recording unless there is a clear reason and everyone agrees.
If you use Microsoft 365, an administrator can control whether Copilot and transcription are allowed in Teams meetings. That gives you one place to set the rule instead of relying on each person to get it right.
Frequently Asked Questions
Is it legal to record a meeting with an AI note taker?
It depends on where everyone in the meeting is. Around a dozen U.S. states and most Australian states require everyone to consent. The UK, federal U.S. law and most U.S. states allow it with one person's consent. In the UK and Europe, you also need to inform people and have a valid reason under data-protection law. The safe approach everywhere is to announce the recording and let people object before it starts.
Does Microsoft Copilot use my meeting data to train its AI?
No. Microsoft states that Copilot in Teams does not use your meeting content, prompts or responses to train its foundation AI models and that the data stays within your organization's Microsoft 365 environment.
Can an AI note taker join a meeting without me knowing?
Yes. Many tools connect to a user's calendar and can auto-join meetings (sometimes ones the user isn't even attending). You can turn auto-join off so the bot only records when someone chooses to start it.
Where are AI note taker recordings stored?
In the cloud. With Microsoft Copilot, the data stays inside your Microsoft 365 tenant. With many third-party tools, recordings sit on the vendor's own servers. Where they live and who can reach them depends on the tool so check its terms.
Should we let staff use Otter or Fireflies for work?
You can but with rules in place. Choose one approved tool, turn off auto-join, announce recording and get consent, check how the tool handles your data and keep it out of legal, HR and confidential client meetings.
Article summary: The software your business already uses has quietly gained AI capabilities. An AI software security audit helps you identify which AI features are active across your tools, what data they can access and whether vendor terms have changed in ways that affect your privacy or compliance obligations. Read more
It usually starts small. Someone uses an AI tool to refine a difficult email. Someone enables an AI add-on inside a SaaS app because it promises to save an hour a week. Someone pastes a paragraph into a chatbot to “make it sound better.”
Then it becomes routine.
Once it is routine, it stops being a simple tool decision and becomes a data governance issue. What is being shared, where it is going and whether you could prove what happened if something goes wrong.
That is the core of shadow AI security.
The goal isn’t to block AI entirely. It is to prevent sensitive data from being exposed in the process.
Shadow AI Security in 2026
Shadow AI is the unsanctioned use of AI tools without IT approval or oversight and is often driven by speed and convenience. The challenge is that the “helpful shortcut” can become a blind spot when IT can’t see what is being used, by whom or with what data.
Shadow AI security matters in 2026 because AI isn’t just a standalone tool employees choose to use. It is increasingly embedded directly into the applications you already rely on. At the same time, it is expanding through plug-ins, extensions and third-party copilots that can tap into business data with very little friction.
There is a human reality in it. 38% of employees admit they have shared sensitive work information with AI tools without permission. It is people trying to work faster but making risky decisions as they go.
That is why Microsoft sees the issue as a data leak problem rather than a productivity problem.
In its guidance on preventing data leaks to shadow AI, the core risk is simple. Employees can use AI tools without proper oversight and sensitive data can end up outside the controls you rely on for governance and compliance.
Here is what many teams overlook. The risk isn’t just which tool someone used. It is what that tool continues to do with the data over time.
This is known as “purpose creep” when data begins to be used in ways that no longer align with its original purpose, disclosures or agreements.
Shadow AI is not limited to one obvious chatbot. It shows up in workflows across marketing, HR, support and engineering and often through browser-based tools and integrations that are easy to adopt and hard to track.
The Two Ways Shadow AI Security Fails
1.) You don’t know what tools are in use or what data is being shared.
Shadow AI isn’t always a shiny new app someone signs up for.
It can be an AI add-on enabled inside an existing platform, a browser extension or a feature that only shows up for certain users. That makes it easy for AI usage to spread without a clear “moment” where IT would normally review or approve it.
It is best to treat this as a visibility problem first. If you can’t reliably discover where AI is being used, you can’t apply consistent controls to prevent data leakage.
2.) You have visibility but no meaningful way to manage or limit it.
Even when you can name the tools, shadow AI security still fails if you can’t enforce consistent behavior.
That typically happens when AI activity lives outside your managed identity systems, bypasses normal logging or isn’t governed by a clear policy defining what is acceptable.
You are left with “known unknowns”. People assume it is happening but no one can document it, standardize it or rein it in.
This can quickly turn into a governance issue. This happens when the organization loses confidence in where data flows and how it is being used across workflows and third parties.
How to Conduct a Shadow AI Audit
A shadow AI audit should feel like routine maintenance rather than a crackdown. The goal is to gain clarity quickly, reduce the most significant risks first and keep the team moving without disruption.
Step 1: Discover Usage Without Disruption
Start by reviewing the signals you already have before sending a company-wide email.
Practical places to look:
- Identity logs: Who is signing in to which tools and whether the account is managed or personal
- Browser and endpoint telemetry on managed devices
- SaaS admin settings and enabled AI features
- A brief and nonjudgmental self-report prompt such as “What AI tools or features are helping you save time right now?”
Shadow AI is often adopted for productivity first rather than because people are trying to bypass security. You will get better answers when you approach discovery as “help us support this safely.”
Step 2: Map the Workflows
Don’t obsess over tool names. Map where AI touches real work.
Build a simple view:
- Workflow
- AI touchpoint
- Input type
- Output use
- Owner
Step 3: Classify What data is Being Put into AI
This is where shadow AI security becomes practical.
Use simple buckets that your team can apply without legal translation:
- Public
- Internal
- Confidential
- Regulated (if relevant)
Step 4: Triage Risk Quickly
You are not aiming to create a perfect inventory. You are focused on identifying the highest risks right now.
A simple scoring model can help you move quickly:
- Sensitivity of the data involved
- Whether access occurs through a personal account or a managed/SSO account
- Clarity around retention and training settings
- Ability to share or export the data
- Availability of audit logging
If you keep this step lightweight, you will avoid the trap of analyzing everything and fixing nothing.
Step 5: Decide on Outcomes
Make decisions that are easy to follow and easy to enforce:
- Approved: Permitted for defined use cases with managed identity and logging wherever possible
- Restricted: Allowed only for low-risk inputs with no sensitive data
- Replaced: Transition the workflow to an approved alternative
- Blocked: Poses unacceptable risk or lacks workable controls
Stop Guessing and Start Governing
Shadow AI security is not about shutting down innovation. It is about making sure sensitive data doesn’t flow into tools you can’t monitor, govern or defend.
A structured shadow AI audit gives you a repeatable process. Identify what is in use, understand where it intersects with real workflows, define clear data boundaries, prioritize the biggest risks and make decisions that hold.
Do it once and you reduce risk right away. Make it a quarterly discipline and shadow AI stops being a surprise.
If you would like help building a practical shadow AI audit for your organization, contact us today. We will help you gain visibility, reduce exposure and put guardrails in place without slowing your team down.
Article Summary: As AI solutions continue to advance, the landscape is also shifting from basic chatbots into more specialized “Agentic AI” systems that execute multistep tasks autonomously. For small businesses, this shift promises increased efficiencies and also creates new security and operational complexities. Success with AI agents will depend on a foundation of clean data and clear processes which will transform AI automation to true business process delegation under human supervision. Early preparation includes auditing workflows for their automation potential, rethinking staff roles and improving data governance.
AI chatbots can answer questions. Now picture an AI that goes further by doing things like updating your CRM, booking appointments and sending emails automatically. This isn’t some far-off future. It is where things are headed in 2026 and beyond as AI shifts from reactive tools to proactive and autonomous agents.
This next wave of AI is called “Agentic AI.” It describes AI that can set a goal, figure out the steps, use the right tools and get the job done on its own. For a small business, that could mean an AI that takes an invoice from inbox to paid or one that runs your whole social media presence. The upside is massive efficiency but it also means you need to be prepared. When AI gets more powerful, having the right controls matters just as much.
What Makes an AI “Agentic”?
Think of the difference between a tool and an employee. A chatbot is a tool you use to help you with tasks while you stay in control. An AI agent is more like a digital employee you give direction to. It has access to systems, can make decisions with set boundaries and learns from outcomes.
A research article on the evolution and architecture of AI agents explains the big shift like this: AI is moving from tools that wait for instructions to systems that work toward goals on their own. Instead of just helping with tasks, AI starts doing the work and makes it possible to hand off whole processes and collaborate with it like a teammate.
The 2026 Opportunity for Your Business
For small businesses, this is about real leverage. Agentic AI can work around the clock, clear out repetitive bottlenecks and cut down errors in routine processes. That means things like personalizing customer experiences at scale or even adjusting supply chains in real time become possible.
This isn’t about replacing your team. It is about leveling them up. AI takes the busywork so your people can focus on strategy, creativity, tough problems and relationships (the things humans do best). Your role shifts too from doing everything yourself to guiding and supervising your AI.
What You Need Before You Launch Agentic AI
Before you hand over your processes to an AI agent, you need to make sure those processes are rock solid. The reasoning is simple: AI will amplify whatever it touches (order or chaos) with equal efficiency. That is why preparation is key. Start with this checklist:
- Clean and Organize Your Data: AI agents make decisions based on the data you give them. Garbage in means not just garbage out. It can lead to major errors. Audit your critical data sources first.
- Document Workflows Clearly: If a human can’t follow a process step by step, an AI won’t be able to either. Map out each workflow in detail before you automate.
Building Your Governance Framework
Just like with human team members, delegating to an AI agent requires oversight. That means setting up clear guardrails by asking a few key questions:
- What decisions can the AI agent make on its own?
- When does it need human approval or guidance?
- What are its spending limits if it handles finances?
- Which data sources is it allowed to access?
Answering these questions lets you build a framework that becomes your company’s rulebook for its “digital employees.”
Security is another critical piece. Every AI agent needs strict access controls following the principle of least privilege. Just as you wouldn’t give an intern full access to the company bank account, you must carefully define which systems and data each agent can touch. Regular audits of agent activity are now a non-negotiable part of good IT hygiene.
Start Preparing Your Business Today
You don’t need to deploy an AI agent immediately but you can start laying the groundwork today. Start by identifying three to five repetitive and rules-based workflows in your business and document them in detail. Then clean up and centralize the data those workflows rely on.
Try experimenting with existing automation tools as a stepping stone. Platforms that connect your apps (like Zapier or Make) let you practice designing triggered and multi-step actions. Thinking this way is the perfect training ground for an agentic AI future.
Embracing the Role of Strategic Supervisor
The businesses that will thrive are the ones that learn to manage a blended workforce of humans and AI agents. Research from Stanford University suggests that key human skills are shifting from information-processing to organizational and interpersonal abilities. In a world with agentic AI, leadership means setting agent goals, defining ethical boundaries, providing creative direction and interpreting outcomes.
Agentic AI is a true force multiplier but it depends on clean data and well-defined processes. It rewards careful preparation and punishes the hasty. By focusing on data integrity and process clarity now, you position your business to adapt and to lead.
Contact us today for a technology consultation on AI integration. We can help you audit workflows and create a roadmap for a reliable and effective adoption.
Article FAQ
What is a simple example of Agentic AI in a small business?
A good example is an AI agent that monitors inventory levels. For example, when stocks run low it contacts pre-approved suppliers, negotiates prices based on preset limits and places a purchase order and does it all autonomously.
Are AI agents expensive to implement for small businesses?
Not necessarily. Most AI agents operate on a subscription model and there are many open-source solutions that you can self-host and run locally. Ideally the larger cost is not the technology but investing in preparing your data and workflows for use by the AI agent.
What is the biggest risk of using autonomous AI agents?
The biggest risk is “unchecked autonomy,” which leads to automation chaos. Implementing an AI agent without clear limits, oversight and audit logs could lead to financial loss, reputational damage and security breaches if the agent makes erroneous decisions or is manipulated.
Artificial intelligence is no longer just a novelty. It is becoming a core part of how businesses get work done. However, not all AI tools are the same. Terms like “co-pilot” and “agent” are often used interchangeably but using the wrong one is like hiring a brilliant strategist just to take notes or putting a meticulous notetaker in charge of your entire strategy. You need the right AI for the task.Read more
We all agree that public AI tools are fantastic for general tasks such as brainstorming ideas and working with non-sensitive customer data. They help us draft quick emails, write marketing copy and even summarize complex reports in seconds. However, despite the efficiency gains, these digital assistants pose serious risks to businesses handling customer Personally Identifiable Information (PII).
Most public AI tools use the data you provide to train and improve their models. This means every prompt entered into a tool like ChatGPT or Gemini could become part of their training data. A single mistake by an employee could expose client information, internal strategies or proprietary code and processes. As a business owner or manager, it is essential to prevent data leakage before it turns into a serious liability.
Financial and Reputational Protection
Integrating AI into your business workflows is essential for staying competitive but doing it safely is your top priority. The cost of a data leak resulting from careless AI use far outweighs the cost of preventative measures. A single mistake by an employee could expose internal strategies, proprietary code or sensitive client information. This can lead to devastating financial losses from regulatory fines, loss of competitive advantage and the long-term damage to your company's reputation.
Consider the real-world example of Samsung in 2023. Multiple employees at the company's semiconductor division (in a rush for efficiency) accidentally leaked confidential data by pasting it into ChatGPT. The leaks included source code for new semiconductors and confidential meeting recordings which were then retained by the public AI model for training. This wasn't a sophisticated cyberattack. It was human error resulting from a lack of clear policy and technical guardrails. The result was that Samsung had to implement a company-wide ban on generative AI tools to prevent future breaches.
6 Prevention Strategies
Here are six practical strategies to secure your interactions with AI tools and build a culture of security awareness.
1. Establish a Clear AI Security Policy
When it comes to something this critical, guesswork won’t cut it. Your first line of defense is a formal policy that clearly outlines how public AI tools should be used. This policy must define what counts as confidential information and specify which data should never be entered into a public AI model such as social security numbers, financial records, merger discussions or product roadmaps.
Educate your team on this policy during onboarding and reinforce it with quarterly refresher sessions to ensure everyone understands the serious consequences of non-compliance. A clear policy removes ambiguity and establishes firm security standards.
2. Mandate the Use of Dedicated Business Accounts
Free public AI tools often include hidden data-handling terms because their primary goal is improving the model. Upgrading to business tiers such as ChatGPT Team or Enterprise, Google Workspace or Microsoft Copilot for Microsoft 365 is essential. These commercial agreements explicitly state that customer data is not used to train models. By contrast, free or Plus versions of ChatGPT use customer data for model training by default (though users can adjust settings to limit this).
The data privacy guarantees provided by commercial AI vendors ensure that your business inputs will not be used to train public models. This establishes a critical technical and legal barrier between your sensitive information and the open internet. With these business-tier agreements, you are not just purchasing features. You are securing robust AI privacy and compliance assurances from the vendor.
3. Implement Data Loss Prevention Solutions with AI Prompt Protection
Human error and intentional misuse are unavoidable. An employee might accidentally paste confidential information into a public AI chat or attempt to upload a document containing sensitive client PII. You can prevent this by implementing data loss prevention (DLP) solutions that stop data leakage at the source. Tools like Cloudflare DLP and Microsoft Purview offer advanced browser-level context analysis and scan prompts and file uploads in real time before they ever reach the AI platform.
These DLP solutions automatically block data flagged as sensitive or confidential. For unclassified data, they use contextual analysis to redact information that matches predefined patterns like credit card numbers, project code names or internal file paths. Together these safeguards create a safety net that detects, logs and reports errors before they escalate into serious data breaches.
4. Conduct Continuous Employee Training
Even the most airtight AI use policy is useless if all it does is sit in a shared folder. Security is a living practice that evolves as the threats advance and memos or basic compliance lectures are never enough.
Conduct interactive workshops where employees practice crafting safe and effective prompts using real-world scenarios from their daily tasks. This hands-on training teaches them to de-identify sensitive data before analysis and turns staff into active participants in data security while still leveraging AI for efficiency.
5. Conduct Regular Audits of AI Tool Usage and Logs
Any security program only works if it is actively monitored. You need clear visibility into how your teams are using public AI tools. Business-grade tiers provide admin dashboards. Make it a habit to review these weekly or monthly. Watch for unusual activity, patterns or alerts that could signal potential policy violations before they become a problem.
Audits are never about assigning blame. They are about identifying gaps in training or weaknesses in your technology stack. Reviewing logs might help you discover which team or department needs extra guidance or indicate areas to refine and close loopholes.
6. Cultivate a Culture of Security Mindfulness
Even the best policies and technical controls can fail without a culture that supports them. Business leaders must lead by example and promote secure AI practices and encourage employees to ask questions without fear of reprimand.
This cultural shift turns security into everyone’s responsibility by creating collective vigilance that outperforms any single tool. Your team becomes your strongest line of defense in protecting your data.
Make AI Safety a Core Business Practice
Integrating AI into your business workflows is no longer optional. It is essential for staying competitive and boosting efficiency. That makes doing it safely and responsibly your top priority. The six strategies we have outlined provide a strong foundation to harness AI’s potential while protecting your most valuable data.
Take the next step toward secure AI adoption. Contact us today to formalize your approach and safeguard your business.
ChatGPT and other generative AI tools (such as DALL-E) offer significant benefits for businesses. However, without proper governance, these tools can quickly become a liability rather than an asset. Unfortunately, many companies adopt AI without clear policies or oversight.
Only 5% of U.S. executives surveyed by KPMG have a mature and responsible AI governance program. Another 49% plan to establish one in the future but have not yet done so. Based on these statistics, while many organizations see the importance of responsible AI, most are still unprepared to manage it effectively.
Looking to ensure your AI tools are secure, compliant and delivering real value? This article outlines practical strategies for governing generative AI and highlights the key areas organizations need to prioritize.
Benefits of Generative AI to Businesses
Businesses are embracing generative AI because it automates complex tasks, streamlines workflows and speeds up processes. Tools such as ChatGPT can create content, generate reports and summarize information in seconds. AI is also proving highly effective in customer support by automatically sorting queries and directing them to the right team member.
According to the National Institute of Standards and Technology (NIST), generative AI technologies can improve decision-making, optimize workflows and support innovation across industries. All these benefits aim for greater productivity, streamlined operations and more efficient business performance.
5 Essential Rules to Govern ChatGPT and AI
Managing ChatGPT and other AI tools isn’t just about staying compliant. It is about keeping control and earning client trust. Follow these five rules to set smart, safe and effective AI boundaries in your organization.
Rule 1. Set Clear Boundaries Before You Begin
A solid AI policy begins with clear boundaries for where you can or cannot use generative AI. Without these boundaries, teams may misuse the tools and expose confidential data. Clear ownership keeps innovation safe and focused. Ensure that employees understand the regulations to help them use AI confidently and effectively. Since regulations and business goals can change, these limits should be updated regularly.
Rule 2: Always Keep Humans in the Loop
Generative AI can create content that sounds convincing but may be completely inaccurate. Every effective AI policy needs human oversight. AI should assist people rather than replace them. It can speed up drafting, automate repetitive tasks and uncover insights but only a human can verify accuracy, tone and intent.
This means that no AI-generated content should be published or shared publicly without human review. The same applies to internal documents that affect key decisions. Humans bring the context and judgment that AI lacks.
Moreover, the U.S. Copyright Office has clarified that purely AI-generated content that lacks significant human input is not protected by copyright. This means your company cannot legally own fully automated creations. Only human input can help maintain both originality and ownership.
Rule 3: Ensure Transparency and Keep Logs
Transparency is essential in AI governance. You need to know how, when and why AI tools are being used across your organization. Otherwise, it will be difficult to identify risks or respond to problems effectively.
A good policy requires logging all AI interactions. This includes prompts, model versions, timestamps and the person responsible. These logs create an audit trail that protects your organization during compliance reviews or disputes. Additionally, logs help you learn. Over time, you can analyze usage patterns to identify where AI performs well and where it produces errors.
Rule 4: Intellectual Property and Data Protection
Intellectual property and data management are critical concerns in AI. Whenever you type a prompt into ChatGPT, you risk sharing information with a third party. If the prompt includes confidential or client-specific details, you may have already violated privacy rules or contractual agreements.
To manage your business effectively, your AI policy should clearly define what data can and cannot be used with AI. Employees should never enter confidential information or information protected by nondisclosure agreements into public tools.
Rule 5: Make AI Governance a Continuous Practice
AI governance isn’t a one-and-done policy. It is an ongoing process. AI evolves so quickly that regulations written today can become outdated within months. Your policy should include a framework for regular review, updates and retraining.
Ideally, you should schedule quarterly policy evaluations. Assess how your team uses AI, where risks have emerged and which technologies or regulations have changed. When necessary, adjust your rules to reflect new realities.
Why These Rules Matter More Than Ever
These rules work together to create a solid foundation for using AI responsibly. As AI becomes part of daily operations, having clear guidelines keeps your organization on the right side of ethics and the law.
The benefits of a well-governed AI use policy go beyond minimizing risk. It enhances efficiency, builds client trust and helps your teams adapt more quickly to new technologies by providing clear expectations. Following these guidelines also strengthens your brand’s credibility and shows partners and clients that you operate responsibly and thoughtfully.
Turn Policy into a Competitive Advantage
Generative AI can boost productivity, creativity and innovation but only when guided by a strong policy framework. AI governance doesn’t hinder progress. It ensures that progress is safe. By following the five rules outlined above, you can transform AI from a risky experiment into a valuable business asset.
We help businesses build strong frameworks for AI governance. Whether you are busy running your operations or looking for guidance on using AI responsibly, we have solutions to support you. Contact us today to create your AI Policy Playbook and turn responsible innovation into a competitive advantage.
In recent years, generative AI tools like ChatGPT have rapidly gained adoption among both individuals and businesses. Within organizations, teams are leveraging these tools for a wide range of tasks including drafting emails, brainstorming ideas, writing code, analyzing data, summarizing reports and even creating media such as videos and images.Read more
Most organizations have realized that AI is not a sentient system looking to take over the world. It is an invaluable tool. They have come to utilize it to improve their productivity and efficiency. AI solutions have been installed at an astounding rate. Some are used to automate repetitive tasks and to provide enriched data analysis on a previously unrealized level. While this can certainly boost productivity, it is also troubling from a data security, privacy and cyber threat perspective.
The crux of this conundrum is how the power of AI can be harnessed to remain competitive while eliminating cybersecurity risks.
The Rise of AI
AI is no longer just a tool for massive enterprises. It is a tool every organization can use. Cloud-based systems and machine learning APIs have become more affordable and necessary in the modern-day business climate for small and medium-sized businesses (SMBs).
AI has become common in the following ways:- Email and meeting scheduling
- Customer service automation
- Sales forecasting
- Document generation and summarization
- Invoice processing
- Data analytics
- Cybersecurity threat detection
AI tools help staff become more efficient and eliminate errors and helps make data-backed decisions. However, organizations need to take steps to limit cybersecurity issues.
AI Adoption Risks
An unfortunate side effect of increasing productivity through the use of AI-based tools is that it also expands the available attack surface for cyber attackers. Organizations must understand that implementing any new technology needs to be done with thoughtful consideration of how it might expose these various threats.
Data Leakage
In order to operate, AI models need data. This can be sensitive customer data, financial information or proprietary work products. If this information needs to be sent to third-party AI models, there must be a clear understanding of how and when this information will be used. In some cases, AI companies can store it, use it for training or even leak this information for public consumption.
Shadow AI
Many employees use AI tools for their daily work. This might include generative platforms or online chatbots. Without proper vetting, these can cause compliance risks.
Overreliance and Automation Bias
Even when using AI tools, it is important for companies to continue their due diligence. Many users consider AI-generated content to always be accurate when it is not. Relying on this information without checking it for accuracy can lead to poor decision-making.
Secure AI and Productivity
The steps necessary to secure potential security risks when utilizing AI tools are relatively straightforward.
Establish an AI Usage Policy
It is critical to set limits and guidelines for AI use prior to installing any AI tools.
Be sure to define:
- Approved AI tools and vendors
- Acceptable use cases
- Prohibited data types
- Data retention practices
Educate users regarding the importance of AI security practices and how to properly use the tools installed to minimize the risk associated with using AI tools.
Choose Enterprise-Grade AI Platforms
One way to secure AI platforms is by ensuring that they offer the following:
- GDPR, HIPAA or SOC 2 compliant
- Data residency controls
- Do not use customer data for training
- Provide encryption for data at rest and in transit
Segment Sensitive Data Access
Adopting role-based access controls (RBAC) provides better restrictions on data access. It allows AI tools access to only specific types of information.
Monitor AI Usage
It is essential to monitor AI usage across the organization to understand what information is being accessed and how it is being utilized including:
- Which users are accessing which tools
- What data is being sent or processed
- Alerts for unusual or risky behavior
AI for Cybersecurity
While concerns exist about AI use regarding security issues, one of the primary uses of AI tools is the detection of cyber threats. Organizations use AI to do the following:
- Threat detection
- Email phishing deterrent
- Endpoint protection
- Automated response
Adopting tools like SentinelOne, Microsoft Defender for Endpoint and CrowdStrike all use AI aspects to detect threats in real-time.
Train Employees About Responsible Use
An unfortunate truth about humans is that they are the weakest link in the chain of cyber defense. Even the strongest defensive stance on cyber threats can be undone with a single click by a single user.
It is important that they receive training regarding the proper use of AI tools so they understand:
- Risks of using AI tools with company data
- AI-generated phishing
- Recognizing AI-generated content
AI With Guardrails
AI tools can transform any organization’s technical landscape and expand what is possible. However, productivity without proper protection is a risk you can’t afford. Contact us today for expert guidance, practical toolkits and resources to help you harness AI safely and effectively.
Imagine one of your employees trying to be efficient and uploading a document containing a client’s personal information into a public artificial intelligence (AI) chatbot to draft the perfect email. It feels harmless in the moment but that single upload places a client’s personal information on a third-party system you don’t control. A well-intended shortcut suddenly turns into a risk with real consequences. With public AI tools multiplying and employees hunting for faster ways to work, keeping client personally identifiable information (PII) from slipping into the wrong hands has become a major priority for every business. The rules outlined below give your team clear and actionable guidance to maintain efficiency while keeping client information safe and secure.Read more