October is Cybersecurity Awareness Month. As such, Automox reinforces its commitment to reducing attack surfaces — an ongoing challenge most organizations face.
The initiative highlights Automox’s hardened platform architecture and leading vendor partnerships to ensure reduced risk of supply chain compromise while providing day-zero support for OS vulnerabilities. Forcing teams to choose between tight budgets and critical security features is just plain wrong. That’s why Automox includes multi-factor authentication (MFA), single sign-on (SSO), role-based access controls (RBAC), audit trails, and API access for all customers at no extra cost.
“Security by design shouldn’t be marketing fluff or a way to extract more revenue from customers,” says Jason Kikta, SVP Product and CISO of Automox. “It’s important to remember that your IT and security tools should reduce risk, not expand your attack surface. Automox proves this commitment through our platform architecture, vendor partnerships, and security-first approach that comes standard — not as an expensive add-on.”
Platform security differentiators include:
- Hardened Architecture: Automox enforces secure practices in every step of product development, from code to deployment. This includes secure agent communication, least-privilege access, and cloud-native architecture with strong encryption built on AWS’s government-certified infrastructure.
- Proactive Vendor Partnerships: As a Microsoft Active Protections Program (MAPP) partner and macOS day-zero support, Automox receives early CVE intelligence, enabling zero-day patching for OS-level vulnerabilities and keeping customers ahead of emerging threats.
- Patch Safe: Third-party patches undergo rigorous scanning through 50+ industry-leading malware detection engines before distribution, ensuring supply chain integrity without compromising speed or coverage.
- Proven Best-in-Class Platform Security: Automox maintains CSA Star, EU-US DPF, GDPR, PCI-DSS v4, SOC2+3, and TX-RAMP certifications, demonstrating adherence to established cloud security standards and best practices.
The Latest
As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...
For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...
I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...
Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...
80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...
40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...
Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...
Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...
Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...
Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...