The 2026 MSSP Blueprint: Supercharging AI SecOps with Observability 

Guest blog courtesy of Palo Alto

Observability has become essential for AI Security Operations. It provides ongoing telemetry and clear visibility across endpoints, cloud workloads, and identity platforms. As more organizations adopt AI and automation, traditional perimeter defenses and manual triage are no longer sufficient to address emerging threats. By adding continuous observability to security operations, teams can shift from reacting to incidents to proactively defending their systems. This approach also helps maintain operational resilience and build digital trust. 

The Modern Threat Landscape and Machine Identities 

Today’s organizations rely on non-human identities like API tokens, automated service accounts, and AI agents. According to Palo Alto Networks, enterprise systems now have 82 machine identities for every human one, shifting the main attack surface. Attackers are increasingly likely to steal valid credentials to access critical systems and hide within regular service traffic, rather than target software flaws. 

The 2026 Palo Alto Networks Unit 42® Global Incident Response Report found that almost 90% of incidents involved identity weaknesses. Attackers are also moving faster, with the average time from initial access to data exfiltration now just 72 minutes, four times as fast as last year. If organizations rely on manual triage and ticketing during this short window, they risk being compromised quickly. Using separate security tools also increases downtime and slows response, which raises costs. 

The emergence of autonomous insider threats represents a significant increase in enterprise risk. AI tools and virtual assistants often have broad access to company databases and backend APIs, which expands the attack surface. Without ongoing runtime monitoring, attackers can use prompt injection or change API payloads to take over these agents. Because AI agents are trusted within the network, malicious commands can extract sensitive data and bypass traditional defenses. 

To manage these risks, organizations need to continuously monitor AI agent activity and use detailed logs to detect unusual or unauthorized actions immediately. It’s important to give AI agents only the access they need for their jobs. Security teams should use Just-in-Time access for sensitive tasks, regularly check permission levels, and require multiple approvals for critical commands. Strict controls on prompt inputs and the use of runtime AI Firewalls can also help block suspicious actions before they happen. 

To put these strategies into practice, security leaders should first review all AI agent and machine identity permissions to identify accounts with excessive or unnecessary access. Testing runtime AI Firewalls in a safe environment helps quickly detect risky behavior and verify that prompt injection attacks are blocked. Setting up automated logging and alerts for unusual AI agent actions ensures teams are notified of suspicious activity right away. These steps create a solid base for ongoing observability and better control from the start. 

Modernizing Architecture: Platformization and Control 

To address compressed attack windows and the rise of non-human identities, organizations must evolve toward platformization. Integrating endpoints, cloud environments, SaaS applications, and identity systems into a single automated defense layer eliminates visibility gaps and streamlines runtime telemetry. 

When evaluating and selecting an AI native SOC platform, MSSPs should look for solutions that natively aggregate telemetry across all environments without relying on complex custom integrations. Vendor evaluation should prioritize the capability to ingest high-velocity data, enforce Zero Standing Privileges, and deliver real-time runtime protection for AI agents. 

With an integrated platform, analysts shift to a more supervisory role. Using an AI-native SOC platform like Palo Alto Networks Cortex XSIAM®, automated systems can handle up to 90% of routine security alerts by connecting data from different sources. This lets analysts focus on higher-value work like threat hunting, policy improvement, and governance. To make this shift, security leaders should create a clear transition plan, communicate new roles, and offer structured training and hands-on learning. Reskilling can include workshops on governance, analytics, and automated tools, as well as mentorship to facilitate knowledge sharing. Leaders should also update SOC metrics to track automated response times, the number of automated investigations, and the reduction in manual triage tasks. Regularly reviewing these metrics and gathering feedback will help ensure a smooth transition and support new standards. 

Comparing old security operations to modern AI SecOps with observability shows several important changes in key security areas: 

Two foundational technical controls support this modernized architecture: 

To get started, organizations should take three key steps: review all active machine credentials and non-human identities, set up runtime AI Firewalls to monitor agent prompts in real time, and centralize all telemetry data in an AI SOC platform.