Correlate & explain
Bring alerts, asset information and threat intelligence together. Explain why signals belong to an incident and what evidence supports the verdict.
ARTIFICIAL INTELLIGENCE FOR THE SOC
AI that understands context.
Analysts who stay in control.
Prajnera combines a local language model with security telemetry, detection logic and playbooks to support investigation and orchestrate approved actions.
AI FOR SECURITY
From raw evidence to a reasoned next step.
Investigate in plain language
Let analysts express an investigation question naturally. The copilot drafts queries and helps explain findings in the context of the case.
Generate detection logic
Draft queries for coverage gaps in the languages your SIEM uses. Syntax checks and historical validation support review before deployment.
Plan a hunt
Turn advisories and ATT&CK techniques into hypotheses and searches. Promote validated findings into future detections.
Orchestrate response
Propose a response from the investigation context and use approved playbooks to carry it out.
Document the incident
Draft incident summaries, handover notes and management reports from incident evidence.
ANALYST COPILOT
Ask a security question.
ILLUSTRATIVE INTERACTION
Show all hosts that contacted 203.0.113.45 in the last 30 days.
PRAJNERA WORKFLOW
- Translate the question into the relevant SIEM query.
- Validate the query against the environment’s schema.
- Return evidence for analyst review.
- Propose a hunt or response action within the approval policy.
An example of the copilot workflow, not a live query interface.
TIERED AUTONOMY
Choose what AI may do.
Observe alongside your analysts.
AI produces recommendations for comparison with current operations. Your team executes the response.
Control: establish a baseline and review recommendation quality.
SECURITY FOR AI
The agents helping your SOC
also need oversight.
An AI-generated response recommendation needs more than an answer: it needs supporting evidence, the right permissions, a clear approval path and a verified outcome.
Prajnera’s agent-monitoring vision extends this principle to AI agents across the enterprise. Observe their actions, assess policy deviations and bring the evidence into the SOC.
Explore the monitoring approachAI GUARDRAILS
Make decisions inspectable.
Evidence-grounded outputs
Ground explanations in available telemetry and threat context. Route low-confidence decisions for analyst review.
Untrusted content handling
Treat log content as untrusted input and check commands against an allowlist.
Model oversight
Track accuracy and drift, and version model and prompt changes.
Private inference
Run the local LLM inside your environment so sensitive prompts and logs stay within the chosen trust boundary.
YOUR NEXT STEP
Let’s discuss your SOC and AI agents.
Define the security workflows, agent visibility and human controls your environment needs.