AI & Cybersecurity – The Cost Shift Tradeoff

Agentic AI and automation have opened up radical changes to workspaces. Businesses are not only setting up AI agents to automate tasks they’re switching up entire job profiles for better output. 

But when you shift from employees to automation, you’re also trusting machines to maintain data security. This hasn’t always been a smooth transition. According to IBM, 76% of gen AI projects are not secure, which is exacerbated by unauthorized shadow AI tools. 

AI is the future, but you cannot approach it blindly. Here are some key cybersecurity considerations as you transform a traditional workforce into an AI-powered one:

Moving from Human Labor to AI Models

Replacing employees with AI agents might reduce payroll, but it doesn’t eliminate cost. It simply shifts it. Instead of paying salaries, you’re now investing in AI models, licensing, IT infrastructure, and continuous optimization.

AI agents require state-of-the-art GPU servers, scalable cloud instances like Microsoft Azure, and ongoing model updates to stay relevant. Fine-tuning for your workflows isn’t a one-time job either; it’s a recurring expense.

This shift increases the load on your IT environment in ways many leaders underestimate. Unlike humans, AI models demand low-latency networking and uninterrupted uptime.

The net result? You end up spending more on strengthening IT for LLMs. Unless you don’t adapt your cybersecurity posture to match this shift, your AI advantage could turn into a liability.

From Manual Processes to Automated Systems

Automation is as effective as the setup. With reduced team size in operations and logistics, you are betting on every webhook and API call to work flawlessly.

However, complex automation adds another dependency to your system. What used to be a person checking inventory is now an API call. A failed script might lead to downtime and lost revenue.

And that’s not it. Going fully automated also increases tech stack complexity. Automated systems depend on real-time data flow across myriads of tools, and many of them lack proper data hygiene—letting attackers find a gap before you do.

So when you cut costs of manual operations, make sure your IT environment is ready to keep workflows running and secure.

From On-Site Security to Digital Security

As you automate work, you lose the natural safeguards that came with human oversight. Now, your security perimeter isn’t your office—it’s every device, data stream, and user credential in the cloud.

That shift demands more than a firewall. You’re now guarding access tokens, device endpoints, and identities across platforms like Copilot AI, Microsoft 365, SharePoint, and Azure. One way to address this is with an upgraded cybersecurity stack: endpoint detection and response (EDR), identity and access management (IAM), privileged access controls, and even AI-driven threat detection.

On-site security might have taken a backseat, but you have to modernize your security stack to reflect the shift. But most SMBs don’t have the bandwidth or budget to rebuild their entire security stack. Managed IT services solve this problem by taking security and maintenance off their plates without charging a fortune.

From Employees to Cloud Resources

We have touched upon cloud security risks, but it’s a significant consideration on its own. As you scale back internal IT staff, you’re leaning harder on the cloud. 

Be it AWS or Azure, you cannot just set it and forget it. AI workloads are resource-heavy, and the usage spike can rack up bills faster than you anticipate. On top of running costs, you need network segmentation, identity-based access, encryption policies, and compliance configurations. According to Forrester, regulatory fines increased by 215% YoY globally due to AI data lapses. 

From data retention to minimizing data collection, you have to rethink cloud privacy if you want to turn AI-adoption a success.

From Staff Awareness to Algorithm Trust

When AI takes over tasks like knowledge base management, process monitoring, and access control, you’re no longer relying on trained staff; you’re trusting a model to make judgment calls. 

But algorithms aren’t infallible—they can hallucinate, inherit bias, or be manipulated. AI-driven decisions without checks and balances might affect financial reports, compliance alerts, and security reviews. At the end of the day, you will be legally and operationally responsible for those outcomes.

The best way to reduce the trust issues is by containing exposure. Through audits, accuracy validation, and compliance with frameworks like GDPR, HIPAA, or PCI-DSS, the AI agents can perform highly precise tasks. Failure to protect might make them potential attack vectors.

Cybersecurity Risks That Arise with This Shift

All of these lead to the cybersecurity dilemma: Are you safer with a shift to AI automation?

SANS Institute and OWASP have identified prompt injection and model poisoning to be the top security challenges for LLMs. The worst part is that these aren’t always bugs; they’re structural weaknesses attackers can exploit to extract sensitive data or subvert logic.

Since AI doesn’t work in isolation, it can be manipulated into leaking databases and reports. Now that you have fewer humans in the loop, odd behaviors might go undetected. Without active oversight and behavior-based monitoring, breaches can persist unnoticed for weeks.

This is why adopting AI without redesigning your security model is risky. Fortunately, you don’t have to overhaul your IT system alone. 

Prepare your IT infrastructure for AI

As a managed IT services provider, MyTek handles the IT infrastructure of businesses so they can scale with AI. 

Services like vulnerability assessments, a 360° approach to network security, and cloud maintenance save small businesses thousands of dollars every year. 

From setting up Microsoft services to training your workforce to be more secure, an MSP like MyTek can help you reduce the tradeoffs that may come with upgrading your workforce. 

Get in touch with us today!