Why Security and AI Readiness Must Go Hand in Hand
▼ Summary
– SuperOps and Guardz have bundled PSA, RMM, MDM, and agentic SecOps into one offering to reduce tool-switching, which can consume up to 25% of a technician’s day and cause context loss.
– The bundle aims to lower costs by replacing multiple vendor invoices with one, and MSPs can verify savings by calculating their current multi-vendor costs plus hidden expenses like integration maintenance.
– A connected stack can help close the margin gap between average MSPs (8% net margins) and top performers (18%), but factors like pricing strategy and automation also play a role.
– To evaluate if a vendor is truly “AI-native,” MSPs should ask if the AI accesses connected real-time data, how security and governance of the AI are handled, and if measurable outcomes exist in production environments.
– The pairing of AI readiness and cybersecurity readiness is a current reality for leading MSPs, but most of the market still treats them separately, though this will become necessary soon.
The partnership between SuperOps and Guardz is bringing together PSA, RMM, MDM, and agentic SecOps into a single, unified platform for MSPs. In this discussion, SuperOps CEO Arvind Parthiban and Guardz CEO Dor Eisner explore how a connected tech stack eliminates the time and context lost to constant tool-switching, reduces costs compared to multi-vendor setups, and helps narrow the gap between average MSP margins of 8% and the 18% achieved by top performers. They also break down what it truly means for a platform to be AI-native and why they view security readiness and AI readiness as two sides of the same coin.
SuperOps and Guardz are bundling PSA, RMM, MDM, and agentic SecOps into one offering. How does consolidating these into a single system address the tool-switching that reportedly consumes up to 25% of a technician’s working day?
Arvind Parthiban, CEO, SuperOps:
The 25% figure actually underestimates the real cost because it only measures time. What it misses is the context loss that occurs every time a technician moves between systems. When your PSA doesn’t communicate with your RMM, and neither integrates with your security platform, you’re not just losing minutes, you’re losing momentum and accuracy. A technician resolving a ticket has to mentally reconstruct what happened across three or four different tools before they can take action. That’s where errors happen and where response times stretch.
Consolidation solves this by making context persistent. When operations and security data live in one system, a technician sees the full picture without needing to switch screens. The time savings are real, but the bigger win is decision quality: fewer missed signals, faster resolution, and less cognitive load on every single interaction.
This also lays the groundwork for agentic IT. AI agents depend on connected, real-time context to act autonomously: to triage a ticket, correlate a security signal, or trigger a remediation workflow without waiting for a human to connect the dots. When that context is scattered across disconnected tools, AI cannot perform at its full potential. A unified stack is not just about efficiency, it is the prerequisite for AI to move from automation to autonomy. That is the transformation we are building toward.
The companies describe the bundle as a more affordable package than traditional multi-vendor stacks, available globally at launch. What cost evidence backs that claim, and how can an MSP verify the savings for its own business?
Arvind Parthiban, CEO, SuperOps:
The simplest way for an MSP to verify this is to add up what they are currently paying across their PSA, RMM, MDM, and security vendors, then factor in the hidden costs that do not appear on those invoices. Integration maintenance, staff time spent on tool administration, and the support overhead of managing multiple vendor relationships all carry real costs that most MSPs do not formally track.
The bundle replaces multiple line items with one. We are confident that for the vast majority of MSPs running a comparable multi-vendor stack, the total cost comparison will be favorable. We encourage any MSP to run that calculation against their own numbers. We are not asking anyone to take our word for it.
The press release notes that the average MSP operates on net margins of roughly 8%, while top-performing firms reach 18%. How much of that gap can a connected stack close, and what other factors drive the difference?
Arvind Parthiban, CEO, SuperOps:
The stack is a significant contributor, but it is not the whole story. Top-performing MSPs have made a structural decision to stop adding tools and start building leverage. A connected stack is part of that. It reduces the operational overhead that quietly erodes margin on every ticket, every escalation, every onboarding. When your tools share context, your team spends less time administering systems and more time delivering outcomes.
But other factors matter too. Pricing strategy, service packaging, customer mix, and how effectively an MSP has automated routine work all play a role. What we would say is this: you cannot close the margin gap without addressing the stack, but closing the stack problem alone will not get you to better margins. The best operators have done both; they have simplified the foundation and built a business model on top of it that reflects the value they actually deliver.
Both companies describe themselves as AI-native and argue that organizations cannot safely deploy AI without strong security and governance. How should an MSP evaluate whether a vendor’s “AI-native” label reflects capability rather than marketing?
Dor Eisner, CEO, Guardz:
Ask three questions. First: does the AI have access to connected, real-time data, or is it working from isolated data sets within a single tool? AI that can only see part of the picture will always produce partial answers. Second: how does the vendor handle the security and governance of the AI itself? What data does it access, how are decisions logged, and who is accountable when it acts autonomously? Third: can the vendor show you where AI is creating measurable outcomes in production environments today, not in a demo? In our own platform, that means identity, endpoint, and email signals correlate in one data fabric, with human-led MDR accountable for what the AI surfaces.
AI-native should mean the platform was designed from the ground up for AI to operate within it, with the data architecture, the security model, and the automation layer built to support agentic workflows. If a vendor has added an AI feature to a legacy platform, that is not AI-native. The distinction matters because MSPs will be held accountable by their customers for how AI performs in their environment. The foundation has to be real.
The companies frame AI readiness and cybersecurity readiness as inseparable, arguing that neither delivers value without the other. Is that pairing a market reality for most MSPs right now, or is it ahead of where many providers and their customers currently stand?
Dor Eisner, CEO, Guardz:
Honestly, it is both. For the leading edge of the market, the MSPs who are already in strategic conversations with their customers about AI adoption, this is a live reality. They are being asked to govern AI deployments, advise on risk, and ensure that the data AI relies on is trustworthy and protected. For them, security and AI readiness collapsed into the same conversation some time ago.
For the majority of the market, there is still a gap. Many MSPs are still treating security as a separate service line and AI as something they will figure out later. What we are saying is that “later” is arriving faster than most people expect, and the MSPs who wait until their customers force the conversation will be playing catch-up. The pairing is not a prediction; it is a description of where the market is heading. We are launching this partnership now because we believe being early matters.
(Source: Help Net Security)