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Threat detection that thinks faster than attackers move
AI-driven security operations that detect, investigate, and respond to threats in real time. From network anomaly detection to automated incident response — built for organizations where a 4-hour breach detection window is 3 hours too long.
The Challenge
The cybersecurity industry adds tools faster than it reduces risk. Here is what that looks like in practice.
Ponemon Institute data: the average SOC receives 11,000 alerts daily. Analysts can investigate roughly 20-25 per day with proper depth. The math does not work. Organizations either hire more analysts (median SOC analyst salary: $95K, and there are 3.5 million unfilled cybersecurity positions globally per ISC2) or they triage by gut feeling. Most choose gut feeling. Attackers know this — they deliberately generate noise to bury real intrusions in the alert flood.
IBM's 2024 Cost of a Data Breach report: 258 days from breach to containment. That is 8.5 months of an attacker living inside your network, exfiltrating data, escalating privileges, and establishing persistence. The average cost: $4.88 million globally, $9.36 million in the US. Organizations that deployed AI-based security automation cut that cost by $2.22 million on average. The ROI is not theoretical.
The average enterprise runs 76 security tools from 45 vendors. Each tool generates its own alerts, its own logs, its own dashboard. None of them talk to each other properly. A lateral movement attack that touches the firewall, endpoint, and cloud console shows up as three unrelated low-priority alerts across three different tools. No human correlates them in time. By the time someone does, the attacker has domain admin.
CISO teams spend 40-60% of their time on compliance reporting — preparing for audits, generating evidence, filling out questionnaires. That is time not spent on actual threat hunting. The irony: organizations that pass every compliance audit still get breached. Compliance proves you checked boxes. Security requires active detection and response. Most organizations fund the former and starve the latter.
How It Works
Five stages from raw telemetry to automated containment — the entire pipeline executes in under four minutes for confirmed threats.
The platform ingests data from every security-relevant source — SIEM logs, EDR telemetry, firewall events, cloud audit trails, DNS queries, identity provider activity, and email gateway metadata. Raw data from 76 tools (the enterprise average, per Panaseer's 2024 report) gets normalized into a unified event schema. Without normalization, a failed login on Active Directory and a blocked SSH attempt on a Linux server look like unrelated events. After normalization, they are two data points in the same brute-force campaign.
Over a 2-4 week observation period, the system constructs behavioral profiles for every entity — users, devices, applications, network segments. What does normal look like for this database administrator at 10 AM on a Tuesday versus 2 AM on a Saturday? Darktrace's research on enterprise behavioral analysis found that 94% of novel attacks deviate from established behavioral baselines within the first three actions. The baseline is not static — it adapts to role changes, seasonal patterns, and organizational shifts.
Incoming events are scored against behavioral baselines using ensemble models — isolation forests for volumetric anomalies, graph neural networks for relationship-based deviations, and sequence models for temporal patterns. A single anomalous event is noise. Three correlated anomalies across different data sources within a time window become a signal. IBM's X-Force research showed that attacks involving lateral movement touch an average of 5.2 distinct security data sources — correlation across those sources is what separates detection from alert noise.
Correlated signals map to the MITRE ATT&CK framework — specific tactics, techniques, and procedures. The system assigns a confidence score based on signal strength, entity criticality, and external threat intelligence context. A low-confidence anomaly on a developer workstation is a watch item. A high-confidence credential abuse pattern on a domain controller with active threat intel matching is a critical incident. CrowdStrike's 2024 Threat Report confirmed that ATT&CK-mapped detections reduce investigation time by 63% because analysts start with context, not raw data.
Confirmed threats trigger pre-approved response playbooks. Network isolation, account suspension, firewall rule injection, and endpoint quarantine execute within seconds — no waiting for a 3 AM approval call. Simultaneously, the system snapshots affected systems, preserves volatile memory, and generates a forensic timeline. Palo Alto's Unit 42 incident response data shows that organizations with automated containment reduce breach cost by 45% compared to manual-only response. Every automated action is logged with full audit trail for post-incident review.
Performance
Metrics from operational systems — not laboratory tests.
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Threat detection rate
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Mean time to detect
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False positive rate
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Alert reduction
Applications
Each capability operates autonomously within your security perimeter. Deploy individually or as an integrated security intelligence layer.
Baselines normal network behavior across users, devices, and applications — then flags deviations that rule-based systems miss. A database server suddenly sending 4GB outbound at 2 AM? Flagged. An executive's laptop making DNS queries to a domain registered 6 hours ago? Flagged. Not by matching a signature. By understanding that this behavior is abnormal for this entity at this time.
When an alert fires, the AI investigates before a human touches it. Correlates the alert with user behavior history, asset criticality, threat intelligence feeds, and related events across all security tools. Produces a structured investigation report: what happened, what is affected, how confident the system is, and a recommended response. Analysts review conclusions, not raw logs.
Monitors user behavior patterns — file access, login times, data transfers, privilege usage — and detects deviations that suggest compromise or malicious intent. An employee downloading 10x their normal volume of files in the week before resignation? Flagged with context. Not surveillance. Pattern recognition applied to access telemetry that already exists in your logs but nobody reviews.
Analyzes email content, sender behavior, URL patterns, and attachment characteristics to catch phishing that bypasses traditional filters. Detects BEC (business email compromise) by recognizing when a CFO's email style suddenly changes or when a vendor sends an invoice from a domain registered last Tuesday. Also scans internal messages — because 30% of phishing now happens via Slack and Teams, not email.
Your scan found 14,000 vulnerabilities. Which ones matter? AI correlates vulnerability data with asset exposure, exploit availability, attacker activity in your sector, and business impact to rank the 47 that actually pose risk right now. Not CVSS scores — contextual risk scores that factor in your specific environment, your industry's threat landscape, and whether an exploit is being actively used in the wild.
Monitors AWS, Azure, and GCP configurations in real time for misconfigurations, policy violations, and exposed assets. S3 bucket made public? Caught in seconds. IAM role with wildcard permissions created? Flagged with remediation guidance. Tracks configuration drift continuously — not once a quarter during the audit.
Supplements your existing EDR by applying behavioral analysis to endpoint telemetry. Detects fileless malware, living-off-the-land attacks, and process injection that signature-based EDR misses. When CrowdStrike or SentinelOne says a process is suspicious, our AI adds context: is this part of a larger attack chain? What else on this network is exhibiting similar behavior?
Maps your security controls to regulatory frameworks — SOC 2, ISO 27001, PCI-DSS, HIPAA, DPDPA — and continuously monitors compliance status. Generates audit-ready evidence automatically. When a control drifts out of compliance, the system alerts before the auditor finds it. Reduces compliance reporting effort by 70% while actually improving security posture.
Ingests feeds from MITRE ATT&CK, commercial threat intel, CERT-In advisories, dark web monitoring, and industry ISACs. Correlates external intelligence with your internal telemetry in real time. When a new malware variant targeting your industry appears in threat intel, the system immediately scans your environment for indicators of compromise. Not a dashboard you check weekly. A system that acts the moment intelligence becomes relevant.
Pre-defined response actions that execute automatically when specific threat conditions are confirmed. Isolate a compromised endpoint from the network. Block a malicious IP across all firewalls. Disable a compromised account. Snapshot the affected system for forensics. All within seconds of confirmed detection — not after a 45-minute escalation call to get approval at 3 AM.
Industry Applications
Specific applications across operating environments — not generic industry labels.
Deployment
We deploy where your operations live — cloud, on-premise, or at the edge. The architecture serves your governance and latency needs, not the other way around.
Managed deployment on your preferred cloud provider. Rapid scaling, minimal infrastructure overhead.
Full deployment within your data center. Complete data sovereignty and infrastructure control.
Processing at the data source for latency-sensitive applications. Sub-second response times.
Frequently Asked
Traditional security tools match patterns — known malware signatures, predefined rules, static thresholds. They catch what they have seen before. AI cybersecurity learns what normal looks like in your specific environment and flags what deviates. The practical difference: a rule-based SIEM generates 11,000 alerts per day because every deviation from a static threshold triggers a notification. AI-based detection generates 200 high-confidence findings because it understands context — this user, this device, this time, this network segment. That is the difference between alert fatigue and actual threat detection.
No. It sits on top of them. Your SIEM still collects logs. Your EDR still monitors endpoints. This platform ingests data from all of those tools, correlates across them, and applies intelligence that no single tool provides on its own. Think of it as the analyst layer — the one that reads all the data from all your tools simultaneously and connects the dots that a human team of 50 could not connect fast enough.
That is precisely the point. Signature-based detection is retrospective — it catches threats after someone else gets breached first and a signature is published. Behavioral detection catches zero-days by identifying actions that deviate from established baselines regardless of the specific technique. A new ransomware variant that encrypts files using a novel method still exhibits anomalous file access patterns, unusual process behavior, and abnormal network traffic. The AI detects the behavior, not the signature.
Under 0.1% after the 4-week baseline period. But the metric that actually matters is analyst efficiency: how many findings require investigation versus how many are immediately actionable. In a typical deployment, 85% of alerts that would have reached an analyst in a traditional SOC are resolved automatically. The remaining 15% arrive with a full investigation report, so the analyst spends 5 minutes deciding instead of 45 minutes researching.
AI Video Intelligence provides physical security — who entered the building, what is happening on camera. AI Cybersecurity provides digital security — who is inside the network, what are they doing with data. Together, they create a unified security intelligence layer. A badge swipe in Delhi followed by a VPN login from Lagos 10 minutes later? That correlation requires both physical and digital intelligence. Enterprise AI Agents can then execute response playbooks across both domains.
Yes. We deploy in defense and critical infrastructure environments where internet connectivity is not an option. The AI models run entirely on-premises. Threat intelligence updates are delivered via secure transfer mechanisms. No data leaves your perimeter. This is not a cloud-first product with an on-prem option bolted on — the architecture supports full air-gap from the ground up.
Detection begins immediately using pre-trained models and known threat indicators. Behavioral baselines that are specific to your environment take 2-4 weeks to establish. By week six, false positive rates stabilize below 0.1%. The system never stops learning — it improves continuously as your environment evolves. The question is not when it becomes effective. The question is how long you can afford to wait before deploying it.
SOC 2 Type II, ISO 27001, PCI-DSS 4.0, HIPAA, NIST CSF, CIS Controls, and India's DPDPA. The system maps your security controls to framework requirements and continuously monitors compliance status. When a control drifts — a firewall rule change that violates PCI-DSS requirement 1.2, for instance — you know within minutes, not at the next audit. Evidence collection is automatic. Report generation is one click.
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