Today’s landscape demands more than just reactive defenses. The 2025 PwC midyear AI predictions update reveals that 88% of executives plan to increase AI-related budgets over the next year, signaling that organizations now view AI as a strategic imperative, not just a future possibility. Yet even as investment accelerates, 28% of those same executives name “lack of trust” as a top barrier to realizing AI’s full potential.
For managed service providers (MSPs) and IT leaders, this contrast is critical: AI tools offer real promise for boosting detection, automating response, and scaling defense across multiple clients. But left unchecked, AI also introduces risks, from false positives and governance gaps to adversarial misuse.
In this article, we’ll explore how to tap AI’s upside while embedding responsible practices and safeguards that preserve security, customer trust, and operational accountability.
Key takeaways
- AI cybersecurity delivers faster detection and predictive defense by spotting anomalies, identifying zero-day threats, and reducing time-to-response.
- Investment in AI is accelerating. PwC’s 2025 midyear update found 88% of executives plan to increase AI budgets within the year, yet 28% still cite “lack of trust” as a top barrier, underscoring the need for governance and safeguards.
- Cybercriminals are already weaponizing AI to launch more effective phishing, malware, and ransomware campaigns at an exponential scale.
- AI carries operational risks, including false positives, blind spots, and compliance challenges for data privacy.
- Responsible AI adoption requires safeguards such as governance frameworks, human oversight, and vendor accountability.
- IT providers can achieve scalable, reliable protection by combining AI-driven tools with trained teams and proven cybersecurity frameworks.
What is AI in cybersecurity?
Artificial intelligence (AI) in cybersecurity refers to the use of machine learning, behavioral analytics, and natural language processing to detect, analyze, and respond to cyberthreats more effectively than traditional methods. Instead of relying solely on static signatures or rule-based systems, AI continuously learns from data, including network logs, user behavior, and threat intelligence feeds, to identify anomalies that may indicate malicious activity.
For MSPs and IT departments, AI-enhanced security applications go beyond definitions and into daily operations:
- Threat recognition: AI identifies unusual login attempts, lateral movement across networks, or deviations in data transfers that may signal compromise.
- Automated response: AI-driven systems can isolate endpoints, block malicious IPs, or flag high-priority alerts in seconds, reducing strain on human teams.
- Vulnerability management: AI tools help prioritize patching based on exploit likelihood, ensuring limited resources are spent where they matter most.
- Threat intelligence correlation: AI connects signals across multiple environments, giving IT providers visibility into attack campaigns that span multiple clients.
Unlike legacy tools that generate endless alerts, AI cybersecurity solutions deliver context-aware insights, empowering IT leaders to scale protection without overwhelming their teams.
Key benefits of AI for cybersecurity defenses
AI is transforming how organizations approach cybersecurity, providing both MSPs and IT departments with the ability to detect, respond, and adapt to threats at scale. By pairing advanced analytics with automation, AI strengthens defenses while reducing operational burdens.
Real-time detection and response
AI cybersecurity tools analyze massive volumes of data in real time, spotting anomalies that human teams or rule-based systems may overlook. This includes detecting unusual login activity, identifying zero-day threats, and predicting potential attack paths before exploitation occurs.
Impact for MSPs and IT departments: Faster detection strengthens incident response and limits the operational and reputational damage caused by breaches.
Actionable signals
Whether an IT department is managing thousands of endpoints or an MSP is overseeing dozens of client environments, the volume of alerts and logs can overwhelm security staff. AI reduces alert fatigue by filtering false positives, triaging repetitive tasks, and escalating only high-priority incidents. It can also automate patch prioritization and vulnerability management across multiple systems.
Impact for MSPs and IT departments: AI’s ability to reduce noise allows teams to focus on higher-value remediation and proactive security initiatives instead of being buried in alerts.
Reduced dwell time
AI-driven response engines leverage protection options such as SOAR playbooks to automatically contain threats, isolating compromised devices, blocking malicious IPs, or restricting suspicious accounts in seconds. This dramatically reduces both time-to-detection (TTD) and time-to-response (TTR).
Impact for MSPs and IT departments: Faster detection and containment limits dwell time, downtime, ensures compliance with SLAs or regulatory requirements, and reduces the risk of data loss.
Sophisticated threat detection
Modern attacks leverage polymorphic malware, deepfake phishing, and other evasive tactics that bypass traditional defenses. AI models adapt continuously, identifying new patterns and threats in ways signature-based tools cannot.
Impact for MSPs and IT departments: AI strengthens protection against advanced persistent threats (APTs), zero-day exploits, and AI-powered cyberattacks, providing resilience against an evolving threat landscape.
Predictive threat modeling
AI can identify attack trends across industries, geographies, and infrastructures by analyzing large data sets. This predictive capability enables proactive defenses tailored to an organization’s unique risk profile.
Impact for MSPs and IT departments: Better foresight into emerging threats improves strategic planning and reduces the likelihood of being blindsided by new attack methods.
Compliance and data privacy support
AI enhances compliance monitoring by automatically flagging potential violations, enforcing access controls, and supporting data encryption and monitoring requirements for frameworks such as HIPAA and GDPR.
Impact for MSPs and IT departments: Automating compliance tasks reduces manual workloads and ensures organizations stay audit-ready.
The result: AI empowers IT providers to deliver stronger protection, reduce operational overhead, and scale security programs effectively, even as threats grow in speed and sophistication.
The dark side: Risks of AI in cybersecurity
AI is a double-edged sword. The same technology helping IT teams and MSPs scale protection is also fueling more sophisticated attacks. As threat actors adopt AI, defenders must understand the risks and build safeguards into their strategies.
AI-powered cyberattacks
Cybercriminals are now leveraging generative AI to automate phishing, malware creation, and even ransomware campaigns. Emails that once contained telltale spelling errors are now indistinguishable from legitimate business communications. Deepfake audio and video attacks are also on the rise, making social engineering even harder to spot.
Research conducted by Carnegie Mellon in collaboration with Anthropic shows that agentic AI can “autonomously plan and carry out sophisticated cyberattacks without human intervention.”
Learn more: The dark side: How threat actors are using AI explores how adversaries are weaponizing AI at scale.
False positives and blind spots
AI-only systems are not infallible. Overreliance on automation can generate false positives that disrupt operations or overlook novel threats that fall outside the AI model’s training data. For IT departments, this can mean downtime and productivity loss. For MSPs, it risks SLA violations and client dissatisfaction.
Data privacy and compliance gaps
AI models require large datasets to function effectively, but feeding sensitive client or organizational data into AI systems creates privacy risks. Mismanagement of data can lead to compliance violations under frameworks such as GDPR, HIPAA, or PCI DSS.
For a deeper dive into protecting sensitive information, explore our guide on AI and data protection.
Adversarial manipulation
Attackers can attempt to “poison” AI models by feeding them misleading data, which reduces accuracy and creates blind spots. This risk adds another layer of complexity for organizations adopting AI-driven defenses.
Safeguards for responsible AI use in cybersecurity
The growing adoption of AI in cybersecurity makes governance and safeguards non-negotiable. For MSPs and IT departments, responsible AI adoption means striking the right balance between automation and oversight to ensure tools remain trustworthy, compliant, and effective.
Human-in-the-loop collaboration
AI is a powerful force multiplier, but it cannot replace human judgment. Security analysts bring context, intuition, and strategic decision-making that algorithms lack. The most effective cybersecurity strategies use a human-in-the-loop model, where AI automates detection and response at speed while human experts validate, investigate, and manage complex incidents. This ensures automation accelerates workflows without introducing blind spots or misclassifications.
Data security and privacy
AI systems often rely on massive datasets, raising questions about how information is collected, stored, and processed. Safeguards for sensitive data, along with alignment to privacy frameworks such as GDPR and HIPAA, are essential.
Best practice: Require data handling transparency from vendors and enforce strict encryption and access controls.
Explainable AI and transparency
Complex AI models can be difficult to interpret, which creates blind spots. Explainable AI techniques provide insights into how decisions are made, allowing analysts to validate results and uncover hidden risks.
Best practice: Choose AI solutions that provide explainable outputs and audit logs to support compliance and incident reviews.
Governance and accountability
Clear frameworks are essential to ensure AI use remains ethical, secure, and compliant. MSPs and IT leaders can align with standards such as the NIST AI Risk Management Framework and ISO/IEC 42001.
Best practice: Define accountability structures that cover roles, responsibilities, and escalation processes for AI-related incidents.
Guardrails for ethical use
Responsible AI adoption also means setting ethical boundaries. Organizations need policies that govern how AI is used, what data is fed into models, and how results are applied to client environments. Vendors that prioritize ethical AI practices, such as data minimization and bias mitigation, help IT and MSP teams build client trust.
Vendor accountability
Not all AI cybersecurity solutions are created equal. Partnering with vendors who emphasize security, compliance, and human-in-the-loop design reduces risk. MSPs and IT departments should evaluate potential providers based on transparency, SLAs, and their ability to adapt AI responsibly over time.
The result: By embedding governance, human oversight, and ethical guardrails into AI adoption, IT providers can ensure AI becomes a trusted ally in defense, not another source of risk.
Practical best practices for MSPs and IT leaders adopting AI cybersecurity tools
AI adoption is no longer optional; it’s a competitive necessity. But without clear processes, safeguards, and human oversight, AI can create as many risks as it resolves. MSPs and IT departments can use these best practices to deploy AI responsibly while maximizing its defensive value.
Leveraging cybersecurity solutions that use AI-based technology to detect and alert can dramatically increase the ability of an MSP or IT team to contain threats before they impact data. For more information on picking an endpoint, network, and SaaS protection platform, check out our MDR and SIEM buyer’s guide.
Start with high-value use cases
Rather than deploying AI everywhere at once, focus on areas that deliver immediate impact, such as phishing defense, endpoint monitoring, and vulnerability management. These functions provide measurable improvements in detection and response in the most common attack vectors, improving protection without disrupting core processes.
Implement human-in-the-loop oversight
AI accelerates threat detection and response, but human analysts remain critical for context-aware decision making. Embedding human-in-the-loop practices ensures that AI recommendations are validated, high-severity events receive expert review, and automated actions do not create unnecessary business disruptions.
Vet vendors carefully
Not all AI cybersecurity solutions are built with the same standards of transparency or compliance. MSPs and IT teams should evaluate vendors based on their explainability, audit logs, SLAs, and commitment to responsible AI.
Tip: Ask vendors to demonstrate how their AI models are trained, tested for bias, and supported by human oversight mechanisms.
Train and upskill your teams
AI adoption is most effective when IT and security staff are equipped to work alongside it. Provide training on interpreting AI outputs, validating automated actions, and escalating incidents.
Tip: Encourage staff to become fluent in both AI-enabled tools and traditional security practices to maintain operational resilience.
Establish ongoing performance measurement
AI models evolve, and so do threats. Set clear metrics for false positive rates, response times, and detection accuracy. Regularly review AI performance and tune processes to maintain trust and accountability.
The result: By starting with high-impact use cases, embedding human-in-the-loop oversight, and holding vendors accountable, MSPs and IT leaders can harness AI’s power while maintaining the governance and trust needed for long-term resilience.
How ConnectWise enables responsible AI adoption
IT providers need AI-powered cybersecurity solutions that deliver advanced protection without compromising trust, compliance, or client relationships. ConnectWise integrates AI into its cybersecurity tools to strengthen defenses while keeping human-in-the-loop safeguards front and center.
AI-driven detection and response with ConnectWise SIEM™ and ConnectWise MDR™
ConnectWise SIEM and managed detection and response (MDR) leverage AI to analyze massive volumes of security data, identify anomalies, and detect potential breaches faster than traditional methods. Automated triage reduces noise while human SOC analysts validate and escalate critical incidents, ensuring accuracy and trust.
Smarter monitoring and automation with ConnectWise RMM™
ConnectWise RMM uses AI-powered monitoring to flag unusual endpoint behaviors and automate patching and updates across client environments. This combination of automation and intelligent detection helps IT teams and MSPs scale efficiently while minimizing downtime and security gaps.
Integrated safeguards for responsible adoption
Every AI-powered ConnectWise tool incorporates human oversight, transparency, and auditability, aligning with best practices such as the NIST AI Risk Management Framework. This ensures AI enhances defense without creating blind spots or compliance risks.
Conclusion: Balancing innovation and responsibility
AI is no longer just an emerging technology; it’s becoming the backbone of modern cybersecurity. For MSPs and IT departments, it enables faster detection, automated response, and predictive defense at a scale traditional methods can’t match. But the same capabilities are also empowering adversaries, creating a new generation of AI-driven threats.
The path forward requires balance. By embedding safeguards such as human-in-the-loop oversight, governance frameworks, and vendor accountability, MSPs and IT leaders can harness AI’s advantages without amplifying risks. With the right strategy and tools, such as AI-enabled cybersecurity solutions from ConnectWise, organizations can strengthen defenses, protect client trust, and stay resilient against the evolving threat landscape.
