Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

CISA has called SIEM-as-a-service (SIEMaaS) “the Rosetta Stone” for visibility for good reason. Federal defenders need to see what is happening across increasingly complex environments, understand what matters, and turn that context into action before an adversary achieves its objective.

TL;DR: If your development, staging, and production API servers all trust the same OIDC issuer and audience, Kubernetes will accept the same cached kubectl token across all of them. You should use separate audiences in your API.

You’ve seen the demos. You’ve run the pilots. Maybe you even deployed something that works, but is it scaling? Are your users utilizing it on a daily basis, or as intended? Or is it quietly gathering dust next to the last three tools that were supposed to change everything? Scaling AI the way most vendors describe it—connecting more tools and piping more data into more systems—doesn’t lead to progress or productivity. Rather, it results in additional risk.

The speed of AI integration is rapidly outpacing corporate governance capabilities. Generative AI is now embedded in development workflows and business applications, while agentic AI can make decisions and take actions with limited human intervention. That governance gap is becoming harder to ignore. Gartner predicts that more than 40% of agentic AI projects will be canceled by the end of 2027 because of escalating costs, unclear business value, or inadequate risk controls.

Ask five security leaders what "data security for generative AI" covers and you'll get five different answers. Some may point to an existing DLP rule for ChatGPT, while others would point to an AI acceptable use policy. Some operate under the assumption that their DSPM vendor already handles genAI security. The confusion isn't about effort, as most teams are actively trying to get ahead of GenAI risk.

According to Gartner survey data, 56% of CISOs increased their IAM budgets in 2026 compared to 2025 to curb evolving threats, support AI adoption, and manage complex digital ecosystems. The question is: can IAM cost be optimized without reducing security quality? There are instances where organizations keep paying for overlapping tools, manual provisioning workflows, and help desk tickets that a properly automated platform would eliminate outright.

Every network compliance calendar follows the same pattern: quiet between audits, then a spike of scrambling the week before the next one is due. During the quiet stretch, nobody actually knows whether firewall rules, segmentation, and access policies still hold as configured across the network. The audit does not create compliance. It samples it once, and then everyone moves on until the next one comes due.

Every automated red-teaming vendor finds the planted flaw in its own demo app. That tells you very little about how the tool will behave on your APIs, with your login flows, your token lifetimes and your developers. The programs we see struggle usually stall on the work around detection: writing auth scripts, keeping tokens alive, arguing over whether a finding is real, and re-testing fixes by hand. That work lands on a small number of senior security engineers.