Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI agents can reason through tasks, call tools, and adapt their next steps based on intermediate results. That flexibility is useful for building agentic applications, but it also creates security risk at runtime: A prompt injection attempt can change the agent’s instructions, a malicious request can try to exfiltrate sensitive data, and an unsafe tool call can lead to an action that the application owner did not intend.

AWS has become a default infrastructure platform for many organizations. It offers scale, flexibility and a broad service portfolio. However, for managed service providers (MSPs) and cloud providers, AWS pricing can be difficult to explain, forecast and package profitably. One of the most important cost factors is AWS data transfer cost. Internet egress, or data leaving AWS for the public internet, is the most familiar example.

Cyber resilience depends on more than the strength of individual tools. IT teams need backup, recovery, security, endpoint management and automation to work together with shared context, consistent policy enforcement and reduced operational gaps. Acronis Cyber Protect v17 2026 Q2 continues that approach with new capabilities that expand protection coverage, improve security visibility, accelerate recovery and reduce administrative effort from a single integrated platform.

Accelerating security solutions for small businesses‍ Tagore offers strategic services to small businesses. A partnership that can scale‍ Tagore prioritized finding a managed compliance partner with an established product, dedicated support team, and rapid release rate. Standing out from competitors‍ Tagore's partnership with Vanta enhances its strategic focus and deepens client value, creating differentiation in a competitive market.

A fully managed, end-to-end CrowdStrike deployment delivered at enterprise scale by Kroll A global quick-service restaurant brand needed to establish consistent, enterprise-grade protection and centralized visibility to quickly strengthen its security posture. It had to move fast without disrupting global operations or overburdening a lean IT team.

For years, VPNs have been the standard for securing remote access. But today's hybrid work environments, cloud applications, and evolving cyber threats have exposed the limitations of a security model built on implicit trust. Once a user authenticates through a traditional VPN, they often gain broad access to the corporate network. If those credentials are compromised, attackers can move laterally, access sensitive resources, and escalate an attack. Zero Trust takes a different approach.

AI is becoming part of almost everything: customer support, security operations, software development, research, analytics, internal workflows, and, most importantly, drafting emails. AI is increasingly embedded in real business processes, and that creates new risks, not to mention the level of unprecedented access mainly of these platforms to our data. Token torching (a type of Denial-of-Wallet (DoW) attack) is one emerging AI risk.

Here is a number that should stop every CISO cold. Gartner projects that by 2028, 25% of enterprise GenAI applications will face five or more security incidents per year, nearly triple the 9% recorded in 2025. The acceleration is not slowing. Meanwhile, research by OpenText and the Ponemon Institute finds that 79% of organizations have not yet reached full AI maturity in cybersecurity, meaning most enterprises are deploying generative AI without the foundational controls needed to govern it.

You can build a masking demo in an afternoon. Run a regex for credit card patterns, swap the match for XXXX, and ship it. The demo works, the compliance slide says “no PII sent to the LLM,” and everyone moves on. That demo is fooling you by leaving things out. It works because the input is a) clean (card 4111 1111 1111 1111), b) because the only sensitive thing in it is a textbook PII pattern, and c) because nobody downstream ever needs to use the value again.

The first time an MCP (Model Context Protocol) server felt real to me, it wasn't because of a clean demo. It was because of the noise. TL;DR: The harness matters more than the protocol, and the evidence matters more than both. MCP earns its keep when it shortens the path from a good security question to trustworthy evidence, and almost everything interesting about making that work happens in the harness wrapped around the model. In this series, I will cover how to build an MCP for an AI SOC.