Managing AI risk challenges: A detection and response guide for SMBs

Managed IT · Oct 24, 2025

Small and medium-sized businesses are caught in a perfect storm: they're adopting AI faster than ever to stay competitive, yet they're doing so in an already compromised security landscape where

73% have experienced a cybersecurity incident

such as a data breach. This isn't just a statistic, but a reality that's forcing SMBs to rethink their entire approach to compliance and risk management. The question isn't whether AI will create new vulnerabilities in your business, but whether you'll have the right detection and response capabilities in place when those vulnerabilities are exploited.

Recent data highlights the risks that businesses are up against:

Nearly
half (46%) of all breaches
involve customer personal identifiable information, which can include tax identification numbers, emails, phone numbers, and home addresses
Of
SMBs that experienced a cybersecurity incident
, 41% say it disrupted their operations, 23% say it increased security costs, 20% say it resulted in significant unplanned expenses
41% of small businesses that suffered a cyberattack
reported that it cost them at least $100,000
Only
11% of businesses have a formal response plan
, while 37% have an informal plan and 52% have no plan at all. Even if a plan exists, it’s rarely, if ever, tested (30% of cases)

Here are the top five AI compliance and risk challenges SMBs should be aware of, followed by effective mitigation strategies that emphasize the critical role of managed detection and response:

1. Data privacy and security risks

AI systems handle large volumes of sensitive data, making them attractive targets for cybercriminals. Data breaches, identity theft and financial fraud are increasing risks, especially when AI models are poorly secured. Additionally, AI's ability to collect and analyze personal data raises concerns about consent and regulatory compliance under laws like General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). Insider threats and accidental leaks further expose SMBs to legal penalties and reputational damage.

The challenge for SMBs is that traditional security measures often can't keep pace with AI-driven threats. This is where managed detection and response becomes essential, providing the continuous monitoring and rapid response capabilities that many SMBs can't afford to build in-house.

2. Regulatory uncertainty

AI's rapid advancement has outpaced existing regulations, creating legal ambiguity for SMBs. Many compliance laws were not designed for AI, making it difficult for businesses to ensure adherence. As governments introduce new AI regulations, SMBs without dedicated compliance teams may struggle to keep up, risking fines, operational disruptions or forced discontinuation of AI-driven services.

Effective MDR services help bridge this gap by providing compliance reporting capabilities that can assist with multiple frameworks, helping ensure that security incidents are properly documented and reported according to regulatory requirements.

3. Bias and discrimination

AI models can reflect and reinforce biases present in their training data, leading to unfair decisions in hiring, lending and customer service. These biases can result in discrimination lawsuits, regulatory fines and reputational harm. SMBs often lack the resources to detect and address AI bias, making them particularly vulnerable to unintended discriminatory outcomes.

4. Lack of transparency

Many AI systems operate as 'black boxes,' meaning their decision-making processes are difficult to interpret. This lack of transparency creates challenges for businesses needing to explain AI-driven outcomes to regulators, customers or stakeholders. In regulated industries, unclear AI decision-making can result in compliance violations, legal disputes and loss of trust among consumers.

When security incidents occur involving AI systems, the lack of transparency makes it even more critical to have robust detection and response capabilities that can quickly identify what happened and how to contain the damage.

5. Increased operational and reputational risks

AI-driven automation can lead to errors in compliance, fraud detection and risk assessment, potentially resulting in financial penalties and business disruptions. Without proper oversight, AI mistakes can go unchecked, creating legal liabilities. Additionally, AI failures, such as biased decisions or mishandling customer data, can severely damage an SMB's reputation, making recovery difficult in competitive markets.

This is where 24/7 monitoring becomes crucial. Many AI-related security incidents happen outside business hours, and without continuous oversight, small problems can become major breaches.

Top 5 mitigation strategies for small businesses

1. Strengthen governance and compliance monitoring

SMBs should establish governance frameworks to help ensure compliance with evolving regulations. This includes conducting regular risk assessments, assigning compliance officers and implementing AI policies that align with industry standards. Partnering with legal experts and staying informed on regulatory changes can help SMBs navigate the complex compliance landscape.

A robust MDR service can support these efforts by providing the security data aggregation and reporting capabilities needed to demonstrate compliance across multiple frameworks.

2. Enhance data security and privacy protections

To minimize data privacy risks, businesses must adopt robust cybersecurity measures, including data encryption, access controls and anonymization techniques. Regular security audits and compliance checks can help identify vulnerabilities in AI systems. Additionally, SMBs should only collect and store the minimum necessary data to reduce exposure to breaches.

However, even the best preventive measures can fail. This is why having managed detection and response capabilities is essential. It can help catch threats that slip through initial defenses and respond before they cause significant damage.

3. Implement bias detection and fairness audits

Businesses should proactively test AI models for bias and discrimination by using diverse and representative datasets. Conducting fairness audits, leveraging third-party bias detection tools and continuously retraining AI models with unbiased data can help mitigate discriminatory outcomes. Employee training on ethical AI practices also plays a crucial role in addressing AI bias.

4. Maintain human oversight in AI decision-making

While AI can automate compliance tasks, human oversight is essential for ensuring accuracy and fairness. SMBs should establish review processes where human experts validate AI-generated insights before taking action. This hybrid approach reduces the risk of AI errors and strengthens overall compliance efforts.

This principle extends to cybersecurity as well. While automated detection is important, having expert security analysts who can interpret threats in context and respond appropriately is what separates effective MDR from basic monitoring tools.

5. Implement robust incident response plans with 24/7 capabilities

Despite best efforts, AI-related compliance failures can still occur. Having a well-defined incident response plan helps ensure that SMBs can quickly address AI security breaches, biased decisions or regulatory violations. A structured response plan should include steps for identifying, containing, mitigating and reporting AI-related incidents.

For most SMBs, maintaining 24/7 incident response capabilities in-house isn't feasible. This is where partnering with a managed detection and response provider can be helpful. They can help identify threats and keep them contained around the clock.

By implementing these mitigation strategies, SMBs can navigate AI compliance risks more effectively, supporting both legal adherence and ethical AI deployment.

Partnering with TELUS Fully Managed for advanced cybersecurity

With TELUS Fully Managed, our team delivers comprehensive protection against AI-driven cyber threats through its managed detection and response (MDR) service. With over 25 years of experience supporting Canadian businesses, we offer comprehensive cybersecurity benefits including:

Advanced security detection: Centralized platform ingests security data from multiple sources, aggregating data and providing a picture of your IT environment.

24/7 expert threat response: Dedicated security analysts are able to help detect security issues and solve them before they result in system downtime and business disruption.

Compliance and cyber insurance reporting: Advanced reporting capabilities can assist you to fulfill over 150 clauses across multiple compliance frameworks (e.g. PCI-DSS, HIPAA, ISO 27001 and NIST CSF).

Adding TELUS Managed Detection and Response (MDR) service is like having a 24/7 bodyguard for your business. It doesn't just watch for threats; it acts quickly to help stop them before they cause damage, leaving you to focus on what's important: running your business.

A new era of cybersecurity challenges

As AI continues to evolve, so too will the tactics of cybercriminals. For SMBs, staying ahead of these threats requires adapting their cybersecurity strategies to keep pace with technological developments. The traditional approach of relying solely on preventive measures is no longer sufficient. Businesses need the detection and response capabilities that can identify and neutralize threats in real-time.

While the threats are daunting, they are not insurmountable. By staying informed, investing in the right technologies and fostering a culture of vigilance, SMBs can protect themselves against the dark side of AI and support their survival in an increasingly digital world.

The future of cybersecurity is here, and it's powered by AI. How are you protecting your business?

Connect with

a managed IT specialist today

to learn how we can help you with your cybersecurity needs.

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