OUR VISION
AI-DRIVEN SOC + AI AGENT OVERSIGHT
Intelligence to defend.
Visibility to trust.
AI is changing how your organisation works—and how it needs to be protected. Prajnera’s vision brings intelligent security operations and AI agent oversight together.
AI for securitySecurity for AI agentsHuman accountability
One connected SOC lifecycle
DetectInvestigateRespondHuntGovern
OUR MISSION
To unite AI-driven security operations with continuous oversight of AI agents, helping organisations detect threats, govern autonomous actions and keep people accountable for critical decisions.
THE PEOPLE BEHIND THE DEFENCE
A SOC that sees the whole picture.
Your security operations center brings people, processes and technology together to detect and respond to threats. As AI agents begin to act across the enterprise, that responsibility expands.
Understand the SOC challengeTHE SOC CHALLENGE
More signals. More actions.
More to account for.
Too many alerts, too little context
Signals arrive from disconnected tools. Analysts must distinguish meaningful incidents from repeated alerts before they can act.
Manual investigation and response
Collecting evidence and handing cases between teams slows containment and increases the burden on analysts.
Limited analyst capacity
Specialist time goes into repetitive triage, while deeper investigations and proactive hunting compete for attention.
Blind spots across the environment
Missing logs, untested detections and fragmented asset data can leave the SOC with an incomplete view.
AI agents extend the security boundary
An agent may read sensitive data, invoke tools and change systems. Its identity, permissions and actions need security oversight.
Accountability becomes harder
Teams need to reconstruct why an action happened, which evidence informed it and whether a person approved it.
THE PRAJNERA DIFFERENCE
AI for security.
Security for AI agents.
Our vision brings two responsibilities together: helping the SOC respond to threats and helping it oversee the AI agents acting across the organisation.
AI-DRIVEN SOC
Give analysts better context and faster workflows.
Connect detection, investigation, hunting and controlled response across your security stack.
Explore SOC operations
AI AGENT OVERSIGHT
Know what your agents are doing—and why.
Make agent identities, data access, tool calls and decisions part of the security conversation.
Explore agent monitoringTHE PLATFORM
Less fragmentation.
More understanding.
Connect the signals and workflows already in your environment. Give analysts the context to act, and automate the work around them.
Find the incident
inside the noise.
Correlate telemetry, enrich alerts with threat intelligence and prioritise incidents using asset and user context.
AI triageRisk scoring
Carry investigation
through to recovery.
Build the timeline, assess scope and orchestrate containment through playbooks with approval gates for critical actions.
Playbook orchestrationEvidence trails
Look beyond
known detections.
Turn threat intelligence and ATT&CK techniques into hunt hypotheses. Feed validated findings into detections and playbooks.
Proactive huntsThreat intelligence
CONNECTED RESPONSE
From first signal
to verified closure.
Explore an illustrative phishing scenario. Each stage shares context, so the next action starts with a fuller picture.
ILLUSTRATIVE SCENARIO
Separate alerts. A connected incident.
A suspicious email link and unusual PowerShell activity on an endpoint become one incident, enriched with threat intelligence and asset context.
SIGNALS BROUGHT TOGETHER
- Suspicious link flagged by email security
- Unusual process activity detected by EDR
- Shared user and device context correlated
Outcome: one prioritised incident for investigation.
CONTINUOUS COVERAGE
Close the gaps.
Keep them closed.
From discovery to verified fix.
Prioritise vulnerabilities using exploit intelligence, exposure and business impact. Assign owners, track remediation and re-scan to verify closure.
DiscoverPrioritiseRemediateVerify
Know what your SOC can see.
Map SIEM use cases to ATT&CK, identify missing coverage and generate queries for your stack. Validate detections and monitor the logs they depend on.
SPLKQLSigmaAQLEQL
TRUST & CONTROL
Your data.
Your boundaries.
Your decisions.
Keep the AI core inside your environment. Set the level of autonomy that fits your operations.
Private by design
A local LLM keeps sensitive prompts and security telemetry within your trust boundary.
Human oversight, built in
Start in shadow mode. Add approval gates for high-impact actions and let analysts override decisions.
Reasoning you can inspect
Follow the evidence, intelligence and steps behind each verdict, with an auditable record of actions.
Control access and exposure
Support role-based access, SSO, MFA, PII masking and configurable retention policies.
FITS YOUR ENVIRONMENT
Build on the stack
you already trust.
Connect your SIEM, EDR, scanners and service management tools. Confirm the integration scope during discovery.
SplunkMicrosoft SentinelQRadarServiceNowJiraQualysNessusRapid7
On-premises
Run the platform and local LLM in your datacentre, with an air-gapped option for high-security environments.
Private cloud
Deploy in your own cloud account, using your network controls and keys.
Hybrid
Connect on-premises and cloud collectors, with the AI core in your chosen location.
START WITH PROOF
See what changes
in your SOC.
Start with two or three use cases. Establish a baseline, run alongside your analysts and measure the results before a wider rollout.
01 Scope the use cases02 Run a controlled pilot03 Measure the impact
Request a demoinfo@prajnera.ai
A FEW COMMON QUESTIONS
Does Prajnera replace our existing security tools?
Prajnera is designed to connect and orchestrate your existing SIEM, EDR/XDR, scanners and ITSM tools. Discovery establishes which integrations and use cases are in scope.
Can we choose how much to automate?
Yes. The proposed rollout starts in shadow mode, then moves to assisted operations and approval-gated automation. High-impact actions can stay under analyst approval.
How do we measure a pilot?
Capture current detection and response times, alert volumes and analyst effort. Compare these baselines with pilot results, alongside coverage and remediation outcomes.
Where does the AI run?
The local LLM runs inside your environment. Deployment options include on-premises or air-gapped infrastructure, your private cloud, and hybrid estates.