Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

SalatStealer is a Go-based infostealer family first observed in August 2026, built for x86 Windows environments and focused on credential theft. Its documented capabilities include stealing authentication credentials, hiding executing code, and degrading security software.

For MSP service owners and technicians, managed identity threat detection and response comes down to a practical service question: who investigates a compromised client account, and who can contain it after hours? Consistent handling across client environments depends on clear ownership from prevention through recovery. Start by checking the identity coverage already included in your managed detection and response (MDR) service or extended detection and response (XDR) deployment.

Disconnected identity systems create the access risk, audit friction, and IT overhead that identity governance and administration (IGA) is built to close. The 2026 Verizon Data Breach Investigations Report found credential abuse in 39% of breaches. IGA combines policy, certification, and compliance evidence with automated provisioning, deprovisioning, and access requests to keep access aligned with business needs and reduce that risk.

Employees can start using an AI tool and share business information with it before IT has reviewed the service or the data involved. AI security solutions help businesses bring that exposure under control without treating every useful AI interaction as something to ban. This guide is written for SMB IT decision-makers evaluating protection for employees who use public or business generative AI tools, often with support from a managed service provider (MSP).

DLP false positives bury real incidents under benign alerts and push teams to switch off the controls they bought. Most of that noise is configuration. Classify sensitive data before enforcement, pair content matches with identity and destination context, phase policies from simulation to blocking, and read override reasons as a tuning signal. Track the trend per policy, and you can show an auditor what the controls do.

Two of the three layers of agentic AI security already have an owner in your organization, and the third has none. Identity falls to IAM and interaction falls to AppSec, because the controls at both layers extend products those teams already run. The behaviour layer covers what the agent does on the infrastructure once it is running, and it sits between a platform team with no security mandate and a SOC with no sense of what normal looks like for an agent. That gap is where a coerced agent works.

Identity governance cannot tell you which AI agent did something. It records which identity is allowed what. In a cluster, one service account often serves several workloads, and every pod is replaced at the next rollout. As a result, the permission record and the behavior record point at different objects. Detection and investigation need a unit of attribution. The Deployment is the right one. It stays stable across restarts and replicas and changes only when someone ships a rollout.

A cyber loss model for a research organization counts subject records and applies a per-record cost. Personal health information, a notification exercise, a regulatory penalty. ‍ The mechanism that matters in a trial is integrity rather than confidentiality, and it produces a loss that occurs even where nothing was altered. What gets destroyed is the ability to demonstrate that nothing was. ‍