Composable Security Platforms: Integrating Security Data Fabrics into the SOC Stack

The Cost & Compliance Crunch for Indian SOCs

Logs are piling up at 25%+ annual growth, and so are the bills. Indian security teams face a double bind: CERT-In’s directive now mandates 180-day log retention (within India) for compliance, yet storing all that data in a SIEM is prohibitively expensive. Running a SIEM today can feel like paying for every streaming channel 24/7 – even though you only watch a few. SIEM vendors charge by data ingested, so you end up paying for every byte, even the useless noise. It’s no surprise that many enterprises spend crores on SIEM licensing, only to have analysts waste 30% of their time chasing low-value alerts.

“You cannot stop collecting telemetry without creating blind spots, and you cannot keep paying for every byte without draining your budget.”

This catch-22 has left Security Operations Centers (SOCs) struggling. Some try to curb costs by turning off “noisy” data sources (firewalls, DNS, etc.), but that just creates dangerous visibility gaps. Others shorten retention or archive logs offline, but CERT-In’s 180-day rule means dropping data isn’t an option – and retrieving cold archives for an investigation can be painfully slow and costly. The tension is clear: How do you stay compliant and keep full visibility without blowing out your SIEM budget?

Why Traditional Cost-Cutting Falls Short

Typical quick fixes offer only partial relief and introduce new risks:

• Shorter retention periods: Saving less data in SIEM lowers costs but fails compliance audits and hampers investigations. (Six months is the bare minimum now, per CERT-In.)
• Cold archives only: Moving logs out of “hot” SIEM storage saves ingest costs initially, but when you do need those logs, rehydration fees and delays hit hard.
• Dropping noisy sources: Excluding high-volume sources trims volume, but you might miss critical incidents hidden in that data. Blind spots can cripple detection.
• Filtering inside the SIEM: By the time the SIEM discards a log, you’ve already paid to ingest it. Ingest-first, drop-later still racks up the bill for data that provided no security value.

All these measures chip away at the problem without solving it. They force security leaders into an unwinnable choice between cost, compliance, and visibility. What’s needed is a way to ingest everything (to satisfy compliance and visibility) while paying only for what truly matters (to control cost).

A Smarter Middle Path: Databahn’s Intelligent Security Data Pipeline

Instead of sacrificing either logs or budget, forward-thinking teams are turning to Databahn’s intelligent security data pipeline as the connective layer between log sources and the SIEM. This approach keeps every log for compliance but ensures that only the right logs enter your SIEM. By processing data before it hits the SIEM, Databahn ensures high-value, security-relevant events go into premium storage and analytics, while everything else is routed into affordable archives.

Think of it as triage for your telemetry with Databahn at the center:

• Pre-ingestion filtering: Databahn’s AI-powered library of 900+ filtering rules automatically deduplicates, compresses, and drops meaningless data (heartbeats, debug logs, duplicates, etc.) before it ever enters the SIEM. This immediately reduces incoming volume without losing security signal.

• Selective routing: Databahn forks data by value. Critical, security-relevant events stream into your SIEM for real-time detection. Meanwhile, bulk or low-risk logs (needed mainly for compliance or audits) are shunted to cold storage or a data lake. You retain 100% of logs for the required 180 days but only pay SIEM prices for the ones that matter.

• Cold storage compliance: With Databahn, logs that have no immediate security value are automatically routed into low-cost cold storage (cloud or on-prem) designated for compliance. This satisfies CERT-In’s log retention mandate without clogging the SIEM. Importantly, logs remain instantly retrievable for audit or investigation.

• Enrichment & normalization: Databahn enriches and normalizes logs in motion. By the time they hit the SIEM, fewer logs go in but each carries more context. That means streamlined, analysis-ready events instead of raw, noisy telemetry.

Key Outcomes with Databahn:

• 50%+ reduction in SIEM licensing and storage costs (guaranteed minimum savings).
• 900+ out-of-the-box rules cutting noise from day one.
• 100% log retention for 180 days in low-cost storage — ensuring full CERT-In compliance and auditability.

Cutting Costs, Keeping Everything (Proven Results)

This approach fundamentally changes the economics of security data. By aligning cost with value, teams escape the spiral of ever-increasing SIEM bills. In fact, many enterprises achieve 50–70% lower SIEM ingest volumes within weeks, instantly cutting costs in half. Storage footprints shrink as redundant data gets offloaded, often yielding up to 80% savings on storage spend.

Equally important, analysts get relief from alert fatigue. With noisy logs filtered out upstream, the alerts that reach your SOC are fewer but higher fidelity. Teams spend time on real threats, not on torrents of false positives. Compliance is no longer a headache either: every log is still at your fingertips (just in the right place and at the right price). Predictable budgets replace unpredictable spikes, and security leaders no longer have to choose between “spend more” vs. “see less.”

Real-world adopters of this model have reported results like a 60% reduction in daily ingest (saving ₹3+ crore annually) and an 80% log volume reduction in a global deployment – all while maintaining full visibility. The bottom line: SIEM cost reduction and complete visibility are no longer at odds.

“Cut SIEM costs by half and keep every log – it’s now achievable with the right data pipeline strategy.”

Future-Ready, AI-Ready SOC

Beyond immediate savings, a modern data pipeline sets you up for the future. Telemetry volumes will keep growing, and regulations like CERT-In will continue evolving. With an intelligent pipeline in place, your organization can scale and adapt with confidence:

• Need to onboard a new log source? The pipeline can absorb it without ballooning costs.
• Adopting AI-driven analytics? The pipeline’s normalization and context ensure your data is AI-ready out of the gate.
• Changing SIEM vendor or moving to a cloud-native stack? Simply re-point the pipeline – you’re not locked in by where your data lives.

In short, pipeline-driven architectures make your SOC more agile, compliant, and cost-efficient. They turn security data management from a bottleneck into a competitive advantage.

The Bottom Line: Compliance and Cost Savings, No Compromise

Indian enterprises no longer have to choose between meeting CERT-In compliance and controlling SIEM costs. By filtering and routing logs intelligently, you guarantee >50% savings on SIEM and storage spend while retaining 100% of your data for the required 180 days (and beyond). This means no blind spots, no compliance gaps, and no surprise bills – just a leaner, smarter way to handle security telemetry.

Ready to see how this works in practice for your organization? Book a demo now to see it in action.

The world’s data footprint is growing at an astonishing pace – by 2025 we will generate roughly 181 zettabytes of data per year (about 1.45 trillion gigabytes per day). This data deluge spans every device, cloud, and edge node, creating rich insights but also multiplying security and compliance challenges. In such a vast, distributed environment, relying on manual audits and static configurations is no longer tenable. Security teams face a simple fact: as networks grow in size and diversity (cloud, IoT, remote users), traditional perimeter defenses and hand‐crafted rules struggle to keep up. The stakes are high – costly breaches continue to occur when policies lapse. For example, the Equifax breach in 2017 exposed personal information for roughly 147 million people , and Uber’s 2016 hack compromised data for 57 million users. In each case, inconsistent enforcement of data‐handling policies contributed to the problem.

The Compliance Challenge at Scale

Security and compliance at enterprise scale suffer from several interlocking problems. First, data volume and diversity are exploding. Millions of new devices, microservices, and data flows appear each year (IoT alone will generate nearly half of new data). Second, misconfigurations and human error remain rampant: industry reports find that roughly 80% of security exposures stem from misconfigured credentials or policies. A single missing firewall rule or forgotten configuration – as one incident dubbed “the breach that never happened” illustrates – can linger quietly and eventually enable attackers to slip past defenses. Third, regulatory demands are multiplying. Organizations must simultaneously satisfy frameworks like PCI-DSS, HIPAA, GDPR, and NIST, each requiring specific technical controls (segmentation, encryption, logging, etc.) on a tight schedule. Auditors expect continuous evidence that policies are enforced everywhere across on-premises and cloud networks. In practice, many teams find they lack real-time visibility into policy compliance.

• Data Growth and Complexity: Data creation is doubling every few years. Networks now span multi-cloud environments, hybrid infrastructure, and billions of sensors.
• Visibility Gaps: Traditional monitoring often misses drift. A study by XM Cyber found 80% of exposures arise from configuration errors or credential issues), meaning threats hide in blind spots.
• Regulatory Pressure: Frameworks like GDPR, PCI, and new SEC cyber rules demand that data controls (masking, retention, encryption, segmentation) are applied consistently across all systems.

Conventional approaches – shipping everything to a central SIEM or relying on annual audits – simply can’t keep up. When policies are defined in documents rather than machines, enforcement is reactive and errors slip through. The result is “compliance by happenstance” and ever-growing risk.

What Is a Policy-Driven Security Fabric?

A policy-driven security fabric is an architectural approach that embeds security and compliance policies directly into the network and data infrastructure, enforcing them automatically and uniformly at scale. Instead of relying on manually configured devices or point tools, a security fabric uses centralized policy definitions that propagate to every relevant element (switch, cloud service, endpoint, etc.) in real time. Key features include:

• Centralized Policy Management: Security and compliance rules (for example, “encrypt sensitive fields” or “only finance admins access payroll DB”) are defined in one place. A policy engine distributes these rules across networks, clouds, and apps, ensuring a single source of truth.

• Automated Enforcement: Enforcement happens at the network edge or host – for example, via software-defined networking (SDN), network microsegmentation, identity-based access, or data masking agents. Policies automatically trigger actions like encrypting data streams, isolating traffic flows, or dropping non-compliant packets.

• Continuous Compliance Checks: The system continuously monitors activity against policies, alerting on violations and even remediating them. In effect, compliance becomes self-driving: the fabric “knows” which controls must apply to each data flow and enforces them without human intervention.

• Granular Segmentation and Zero Trust: Micro segmentation divides the network into isolated zones (often tied to applications, users, or data categories). By enforcing least-privilege access everywhere, even if an attacker breaches one segment, lateral movement is blocked. This reduces scope for breaches – for example, over 70% of intruders today move laterally once inside, so strict segmentation dramatically curtails that risk.

• Audit and Observability: Every policy decision and data transfer is logged and auditable. Because the fabric is policy-driven, audit trails align with the defined rules – simplifying reporting for auditors.

Unlike legacy systems that “shoot arrows and hope,” a policy-driven fabric automates the chain of trust. When a new application or device comes online, it automatically inherits the relevant policies (for encryption, retention, access, etc.) without manual setup. If a compliance rule changes (e.g. a new data-retention requirement), updating the central policy cascades the change network-wide. This ensures continuous compliance by design.

Industry Trends and Context

The move toward policy-driven security fabrics parallels several industry trends:

• Zero Trust and SASE: Architects increasingly adopt Zero Trust, insisting on per-application, per-user policies. Secure Access Service Edge (SASE) offerings fuse networking and security policies, reflecting this fabric approach.

• Cloud Native and DevOps: With infrastructure-as-code, network configurations and security groups are templated. Policy frameworks (like Kubernetes Network Policies or AWS Security Groups) are used to codify security intent. A security fabric extends this principle across the entire IT estate.

• AI and Automation: Modern tools leverage AI to map data flows and suggest policies (e.g. identifying which data elements should be masked). This accelerates deployment of the fabric without manual analysis.

Real-world incidents highlight why the industry needs this approach. The Equifax breach and Uber cover-up both stemmed from policy gaps. In Uber’s case, hackers stole credentials and exfiltrated data on 57 million users; the company even paid the ransom quietly rather than reporting it. Had a policy-driven fabric been in place (for example, automatically logging and alerting on unauthorized data exfiltration, or enforcing stricter segmentation around customer data), the breach could have been detected or contained sooner. In Equifax’s case, attackers exploited outdated software (no security patch policy) and made off with 147 million records. Today, regulators explicitly require robust patching, encryption, and data-minimization policies – mandates that are easier to meet with automation.

Real-World Applications

Many organizations are already putting these ideas into practice:

• Biotech Manufacturing (Zero Trust): A large pharmaceuticals contract manufacturer applied a policy-driven fabric to its mixed IT/OT environment. By linking identity and device context to security policies, the company implemented over 2,700 micro segmentation rules in a matter of weeks. This was done without major network redesign. As a result, they achieved nearly instant least-privilege access to critical systems and met strict regulatory controls (NIST 800-207, FDA requirements) far faster than with traditional methods.

• Global Financial Networks: Banks and insurers facing multi-jurisdictional regulations have begun using network automation platforms that continuously audit firewall and router configurations against compliance benchmarks. For instance, one financial firm reduced its PCI-DSS compliance reporting time by 50% after adopting a centralized policy engine for firewall rules (internal case study). Now any drift – say, a temporary open port left forgotten – is flagged immediately.

• Cloud Infrastructure at Scale: A multinational e-commerce company leverages a policy fabric to govern data stored across dozens of cloud environments. Data classification tags attached at ingestion automatically route logs and personal data to region-appropriate encrypted storage. Compliance policies (e.g. “no customer SSN leaves EU storage”) are embedded in the fabric, ensuring data sovereignty rules are enforced at every step.

These examples illustrate a common outcome: faster, more reliable compliance. By treating policies as code and applying them uniformly, organizations turn audit prep from a panic-driven scramble into an ongoing automated process.

Building a Resilient Fabric

Implementing a policy-driven fabric requires collaboration between security, network, and compliance teams. Key steps include:

1. Define Clear, Network-Wide Policies: Translate regulations and standards into technical rules. For example, a policy might state “all logins from foreign IPs require MFA” or “credit-card fields must be hashed at ingestion.”

2. Deploy Automated Enforcement Points: Use solutions like SDN controllers, identity-aware proxies, or edge agents that can enforce the policies in real time.

3. Centralize Monitoring and Auditing: Ensure all enforcement points report back to a unified console. Automated tools (e.g. intent-based networking systems) can continuously verify that actual configuration matches the intended policy state.

4. Iterate and Adapt: The fabric should evolve with the environment. New data sources or regulatory updates should map into updated policies, which then roll out automatically across the fabric.

In practice, this often means moving from a checklist mentality (“do we have X control?”) to an architecture where security and compliance are built from the start. Instead of patchy patch management or ad hoc segmentation, the network itself becomes “aware” of compliance constraints.

Conclusion

As data and networks scale to unprecedented levels, manual compliance is a lost cause. A policy-driven security fabric offers a transformative path forward: it embeds compliance into the architecture so that policy enforcement is automatic, continuous, and verifiable. The outcome is security at scale – fewer configuration errors, faster responses, and demonstrable audit trails.

Enterprises that embrace this approach find that compliance can shift from being a cost center to a trust builder. By codifying and automating policies, organizations reduce risk (breaches and fines), save time on audits, and free security teams to focus on strategic defense rather than firefighting. In a world of exploding data and tightening regulations, a policy-driven fabric isn’t just a nice-to-have – it’s the foundation of scalable, future-proof security.

Teams running a Managed Security Service (MSS) are getting overwhelmed with the complexity of growth. Every new customer adds another SIEM, another region, another compliance regime – and delivers another sleepless night for your operations team.

Across the industry, managed security service providers (MSSPs) are discovering the same truth: the cost of complexity grows faster than the revenue it earns. Every tenant brings its own ingestion rules, detection logic, storage geography, and compliance boundaries. What once made sense for ten customers begins to collapse under the weight of 15, 25, and 40 customers.  

This is not a technology failure; it’s an architectural mismatch. MSSPs must contend with and operate multiple platforms and pipelines not generally designed or built for multi-tenancy. They must engage with telemetry architecture that is meant to centralize many sources into a single SIEM, and create ways to federate, manage, and streamline security telemetry in a way that enables SOC operations for multiple users.

The MSSP dilemma: Scaling trust without scaling cost

For most providers, tenant growth directly maps to operational sprawl. Each client has unique SIEM requirements, volume tiers, and compliance needs. Each requires custom integrations, schema alignment, and endless maintenance.  

Three familiar challenges emerge:

1. Replicated toil: onboarding new tenants means rebuilding the same ingestion and normalization flows, often across multiple clouds.
2. Visibility silos: monitoring and governance fragment across tenants and regions, making it hard to see end-to-end health or compliance posture.
3. Unpredictable cost-to-serve: data volumes spike unevenly across tenants, driving up licensing and storage expenses that eat into margins.

It’s the hidden tax of being a multi-tenant provider without a true multi-tenant architecture.

A structural shift: From many pipelines to One Beacon

Modern MSSPs need a control model that scales trust, not toil. They need a structured, infrastructure-driven way to give every tenant autonomy while maintaining centralized intelligence and oversight. We’ve built it, and we call it the Beacon Architecture.

At the heart of the Beacon Architecture is a single, federated control plane that can govern hundreds of isolated data planes below it. Each tenant operates independently with its own routing logic, volume policies, and SIEM integrations, yet all inherit global policies, monitoring, and governance from the Beacon.

The idea is simple: building a system that balances the requirement of guiding every tenant’s telemetry in a way that optimizes for tenant control while enabling centralized governance and management. This isn’t a tweak to traditional data routing; it’s a fundamental redesign around five principles:

Isolation by Design

Each tenant runs its own fully contained data plane – not as a workspace carved out of shared infrastructure. That means you can apply tailored enrichment, normalization, and reduction rules without cross-contamination or schema drift across tenants. Isolation protects autonomy, but the Beacon ensures every tenant still adheres to a consistent governance baseline.  

Operationalizing this requires tagging data at the edge of the collection infrastructure, enabling centralized governance systems to isolate data planes based on these tags.

Policy by Code

Instead of building custom pipelines and collection infrastructure for every client, MSSPs can define policy templates for each tenant and deploy them across existing integrations to deploy faster and with much lower effort.  

A financial services customer in Singapore? Route and store PII for this client in local cloud systems for compliance.  

A healthcare customer in Texas? Apply HIPAA-aligned masking at the edge before ingestion.

Tagging and applying policies for PII at the edge will help MSSPs ensure compliance with data localization and PII norms for customers.

Visibility without Interference

The Beacon provides end-to-end observability – data lineage, drift alerts, pipeline health – across all tenants in a single pane of glass. MSSP operators can now easily track, monitor, and manage data movement. When a customer’s schema changes or a connector stalls, it’s detected automatically and surfaced for approval before it affects operations. It’s the difference between reactive monitoring and proactive assurance.  

Leverage a mesh architecture to ensure resiliency and scalability, while utilizing agentic AI to proactively detect problems and errors more quickly.

Elastic Tenancy

Adding a tenant no longer means adding infrastructure. With a control plane that can spin up isolated data planes on demand, MSSPs can onboard new customers, regions, or sub-brands within hours, not weeks – with zero code duplication. Policy templates and pre-built connectors – including support for different destinations such as SIEMs, SOARs, data lakes, UEBAs, and observability tools – ensures seamless data movement.

Add new tenants through a fast, simple, and flexible process that helps MSSPs focus on providing services and customizations, not on repetitive data engineering.

Federated Intelligence

With isolation and governance handled, MSSPs can now leverage anonymized telemetry patterns across tenants to identify shared threat trends – safely. This federated analytics layer transforms raw, siloed telemetry into contextual knowledge across the portfolio without exposing any customer’s data.

Anonymized pattern tracking to improve security outcomes without adding to the threat surface, thereby growing trust with customers without incurring prohibitively high costs.

The Economic Impact: turning growth into margin

Most MSSPs grow linearly; the cost and effort involved in onboarding each new customer constrain expansion and act as a bottleneck. With the bottleneck, the Beacon Architecture lets MSSPs grow exponentially. When operational effort is decoupled from tenant count, every new customer adds value – not workload.

The outcomes are measurable:

• 50-70% reduction in ingest volumes per tenant through context-aware routing and reduction rules

• 90% faster onboarding using reusable, AI-powered integration templates and automated parsing for custom apps and microservices

• 100% lossless data collection with 99.9%+ pipeline uptime and seamless failover handling, so no data is ever lost

When these efficiencies compound across dozens or hundreds of tenants, the economics change completely: lower engineering overhead, predictable cost-to-serve, and capacity to onboard more customers with the same team, and being able to allocate more bandwidth to strategic security instead of data engineering plumbing.

Governance and Compliance at the edge

Data sovereignty no longer necessitates the creation of separate environments. By tagging and routing data according to policy, MSSPs can automatically enforce where telemetry lives, which region processes it, and which SIEM consumes it. With Beacon, you can also add logic and rules to route less-relevant data to the right data lake and storage endpoint.

PII detection and masking happen at the edge – before data ever crosses borders – giving MSSPs fine-grained control over localization, privacy, and retention. This will enable MSSPs to simplify serving multinational clients or entering new markets without needing to engineer solutions for local compliance.  

In other words: compliance becomes an attribute of the pipeline, not an afterthought of storage.

Operational Reliability as a competitive edge

Every MSSP advertises 24x7 vigilance; few can actually deliver it at the data layer. Most MSSPs use complex workflows, relying on processes, systems, and human expertise to serve their clients. When new sources need to be added, pipelines break, or schemas shift, the tech debt increases, putting pressure on their entire business and operations. 

With self-healing pipelines, automated schema-drift detection, lineage tracking across every route, and simplified no-code source addition, the Beacon Architecture provides the foundation to actually guarantee the kind of always-on vigilance fast-moving businesses need.

Engineers can see – and prove – that every event was collected, transformed, enriched, and delivered successfully. MSSPs and their clients can even measure their data coverage against security frameworks and baselines such as MITRE ATT&CK. These features become a differentiator in client renewals, audits, and compliance assessments.

From Multi-Tenant to Multi-Intelligent

When data is structured, governed, and trusted, it becomes teachable. The same architecture that isolates tenants today can fuel intelligent, cross-tenant analytics tomorrow – from AI-assisted threat correlation to federated reasoning models that learn from patterns across the entire managed estate.  

That evolution – from managing tenants to managing intelligence – is where the next wave of MSSP competitiveness will play out.

Serving Multi-SIEM Enterprises

Enterprises running multiple SIEMs across geographies face the same structural problems as MSSPs: fragmented visibility, inconsistent compliance, and duplicated effort. The Beacon model applies equally well here – CISOs operating multiple SIEMs across geographies can push compliance filtering and policies from the edge, ensuring seamless operations. Each business unit, region, or SOC can maintain its preferred SIEM while the organization gains a unified governance and observability layer – plus the freedom to evaluate or migrate between SIEMs without re-engineering the whole data pipeline.

The future is federated

Beacon Architecture isn’t just a new way to route data – it’s a new way to think about data ownership, autonomy, and assurance in managed security operations. It replaces replication with reuse, fragmentation with federation, and manual oversight with intelligent control. Every MSSP that adopts it moves one step closer to solving the fundamental equation of scale: how to ensure quality operations while adding customers without growing their cost base. They can achieve this by handling more data, and doing so intelligently.

Closing Thought

Multi-tenancy isn’t about hosting more customers. It’s about hosting more confidence.

The MSSPs that master federated control today will define the managed security ecosystem tomorrow – guiding hundreds of tenants with the precision, predictability, and intelligence of a single Beacon.