Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The race to adopt AI agents has created a massive, unmonitored blind spot in the enterprise software supply chain. At the heart of this revolution is the Model Context Protocol (MCP) – an open connectivity standard designed to move AI models (LLMs) out of their passive “chat box” and give them direct active access to your company’s internal systems.

If managed service providers (MSPs) are going to grow as 2026 rolls on, they’re going to have to overcome both new and familiar obstacles in a tough environment. But there is good news for MSPs that are ready to adapt their business models to new market realities. A recent report from Omdia, MSP Trends and Predictions 2026, lays out clearly why MSPs are more likely to struggle to grow in 2026 than they have in past years.

Today, we’re excited to announce that Apono Assistant is now available in Slack. Apono Assistant is Apono’s AI-powered access assistant, built to help engineers request the right Just-in-Time access using natural language — especially in the moments where access forms fall short and users aren’t sure what to request. Now, that same AI experience is available directly in Slack, so engineers can get the access they need without leaving the tools they already rely on every day.

AI agents are rewriting the rules of efficiency, but one hidden flaw could turn them against you. Prompt injection attacks let hackers hijack your AI, steal data, and break safeguards straight through everyday inputs. No code exploit is required, only a clever manipulation. Identity and Access Management (IAM) plays a massive role in AI security to protect at first hand.

Amazon Machine Images (AMIs) are templates for launching and scaling Amazon Elastic Compute Cloud (EC2) instances. Because Amazon EC2 AMIs are reused across environments and automation pipelines, decisions about how you build, source, manage, and share them directly affect your cloud attack surface.

AI risk management is the process of identifying, assessing, and mitigating risks associated with artificial intelligence systems to ensure they are developed and used responsibly. It involves using frameworks like the NIST AI Risk Management Framework to address technical, ethical, and social challenges, including data bias, privacy violations, and security vulnerabilities.

Authors: Dheeraj Kumar and Nitish Singh The Monthly Intelligence Insights report provides a summary of top threats curated, monitored, and analyzed by Securonix Threat Labs in January 2026. The report also includes a synopsis of the threats, indicators of compromise (IoCs), tactics, techniques, and procedures (TTPs), and related tags. Each threat has a comprehensive summary from Threat Labs and search queries from the Threat Research team.

Artificial intelligence is changing the IT landscape in radical, unprecedented ways. It’s rewriting the rules of code generation, automating complex customer service interactions, and providing data insights that used to be impossible to extract, even in recent decades. However, for IT managers and those responsible for keeping the lights on, AI represents a massive shift in infrastructure requirements.