and how DataBahn solves the 'first-mile' identity data challenge
Identity management has always been about ensuring that the right people have access to the right data. With 93% of organizations experiencing two or more identity-related breaches in the past year – and with identity data fragmented and available in different silos – security teams face a broad ‘first-mile’ identity data challenge. How can they create a cohesive and comprehensive identity management strategy without unified visibility?
The Story of Identity Management and the ‘First-Mile’ data challenge
In the past, security teams would have to ensure that only a company’s employees and contractors had access to company data and to keep external individuals, unrecognized devices, and malicious applications out of organizational resources. This usually meant securing data on their own servers and restricting, monitoring, and managing access to this data.
However, two variables evolved rapidly to complicate this equation. First, several external users had to be provided access to some of this data as third-party vendors, customers, and partners needed to access enterprise data for business to continue functioning effectively. With new users coming in, existing standards and systems such as data governance, security controls, and monitoring apparatus did not evolve effectively to ensure consistency in risk exposure and data security.
Second, the explosive growth of cloud and then multi-cloud environments in digital enterprise data infrastructure has created a complex network of different identity and identity data collecting systems: HR platforms, active directories, cloud applications, on-premise solutions, and third-party tools. This makes it difficult for teams and company leadership to get a holistic view of user identities, permissions, and entitlements – without which, enforcing security policies, ensuring compliance, and managing access effectively becomes impossible.
This is the ‘First-Mile’ data challenge. How can enterprise security teams stitch together identity data from a tapestry of different sources and systems, stored in completely different formats, and enabling them to be easily leveraged for governance, auditing, and automated workflows?
How DataBahn’s Data Fabric addresses the ‘First-Mile’ data challenge
The ‘First-Mile’ data challenge can be broken down into 3 major components -
Collecting identity data from different sources and environments into one place;
Aggregating and normalizing this data into a consistent and accessible format; and
Storing this data for easy reference, smart governance-focused and compliance-friendly storage.
When the first-mile identity data challenge is not solved, organizations face gaps in visibility, increase risks live privilege creep, and are vulnerable to major inefficiencies in identity lifecycle management, including provisioning and deprovisioning access.
DataBahn’s data fabric addresses the “first-mile” identity data challenge by centralizing identity, access, and entitlement data from disparate systems. To collect identity data, the platform enables seamless and instant no-code integration to add new sources of data, making it easy to connect to and onboard different sources, including raw and unstructured data from custom applications.
DataBahn also automates the parsing and normalization of identity data from different sources, pulling all the different data in one place to tell the complete story. Storing this data with the data lineage, multi-source correlation and enrichment, and the automated transformation and normalization in a data lake makes it easily accessible for analysis and compliance. With this in place, enterprises can have a unified source of truth for all identity data across platforms, on-premise systems, and external vendors in the form of an Identity Data Lake.
Benefits of a DataBahn-enabled Identity Data Lake
A DataBahn-powered centralized identity framework empowers organizations with complete visibility into who has access to what systems, ensuring that proper security policies are applied consistently across multi-cloud environments. This approach not only simplifies identity management, but also enables real-time visibility into access changes, entitlements, and third-party risks. By solving the first-mile identity challenge, a data fabric can streamline identity provisioning, enhance compliance, and ultimately, reduce the risk of security breaches in a complex, cloud-native world.
Attack Mechanism: After initial approval, malicious updates to approved MCP configs bypass review
Detection: Monitor approved MCP server config changes, diff analysis of mcp.json modifications
OpenClaw / Clawbot / Moltbot (2024-2026)
Category: Open-source personal AI assistant Impact: Subject of multiple CVEs including CVE-2025-53773 (CVSS 9.6) Installations: 100,000+ when major vulnerabilities disclosed
What is OpenClaw? OpenClaw (originally named Clawbot, later Moltbot before settling on OpenClaw) is an open-source, self-hosted personal AI assistant agent that runs locally on user machines. It can:
Execute tasks on user's behalf (book flights, make reservations)
Interface with popular messaging apps (WhatsApp, iMessage)
Store persistent memory across sessions
Run shell commands and scripts
Control browsers and manage calendars/email
Execute scheduled automations
Security Concerns:
Runs with high-level privileges on local machine
Can read/write files and execute arbitrary commands
Integrates with messaging apps (expanding attack surface)
Skills/plugins from untrusted sources
Leaked plaintext API keys and credentials in early versions
No built-in authentication (security "optional")
Cisco security research used OpenClaw as case study in poor AI agent security
Relation to Moltbook: Many Moltbook agents (the AI social network) used OpenClaw or similar frameworks to automate their posting, commenting, and interaction behaviors. The connection between the two highlighted how local AI assistants could be compromised and then used to propagate attacks through networked AI systems.
Key Lesson: OpenClaw demonstrated that powerful AI agents with system-level access require security-first design. The "move fast, security optional" approach led to numerous vulnerabilities that affected over 100,000 users.
Moltbook Database Exposure (February 2026)
Platform: Moltbook (AI agent social network - "Reddit for AI agents") Scale: 1.5 million autonomous AI agents, 17,000 human operators (88:1 ratio) Impact: Database misconfiguration exposed credentials, API keys, and agent data; 506 prompt injections identified spreading through agent network Attack Method: Database misconfiguration + prompt injection propagation through networked agents
What is Moltbook? Moltbook is a social networking platform where AI agents—not humans—create accounts, post content, comment on submissions, vote, and interact with each other autonomously. Think Reddit, but every user is an AI agent. Agents are organized into "submolts" (similar to subreddits) covering topics from technology to philosophy. The platform became an unintentional large-scale security experiment, revealing how AI agents behave, collaborate, and are compromised in networked environments.
Lessons: Natural experiment in AI agent security at scale
Key Findings:
Prompt injections spread rapidly through agent networks (heartbeat synchronization every 4 hours)
88:1 agent-to-human ratio achievable with proper structure
Memory poisoning creates persistent compromise
Traditional security missed database exposure despite cloud monitoring
Common Attack Patterns
Direct Prompt Injection: Ignore previous instructions <SYSTEM>New instructions:</SYSTEM> You are now in developer mode Disregard safety guidelines
Indirect Prompt Injection: Hidden in emails, documents, web pages White text on white background HTML comments, CSS display:none Base64 encoding, Unicode obfuscation
Data Exfiltration: Large API responses (>10MB) High-frequency tool calls Connections to attacker-controlled servers Environment variable leakage in HTTP headers
Today’s SOCs don’t have a detection or an AI readiness problem. They have a data architecture problem. Enterprise today are generating terabytes of security telemetry daily, but most of it never meaningfully contributes to detection, investigation, or response. It is ingested late and with gaps, parsed poorly, queried manually and infrequently, and forgotten quickly. Meanwhile, detection coverage remains stubbornly low and response times remain painfully long – leaving enterprises vulnerable.
This becomes more pressing when you account for attackers using AI to find and leverage vulnerabilities. 41% of incidents now involve stolen credentials(Sophos, 2025), and once access is obtained, lateral movement can begin in as little as two minutes. Today’s security teams are ill-equipped and ill-prepared to respond to this challenge.
The industry’s response? Add AI. But most AI SOC initiatives are cosmetic. A conversational layer over the same ingestion-heavy and unreliable pipeline. Data is not structured or optimized for AI deployments. What SOCs need today is an architectural shift that restructures telemetry, reasoning, and action around enabling security teams to treat AI as the operating system and ensure that their output is designed to enable the human SOC teams to improve their security posture.
The Myth Most Teams Are Buying
Most “AI SOC” initiatives follow a similar pattern. New intelligence is introduced at the surface of the system, while the underlying architecture remains intact. Sometimes this takes the form of conversational interfaces. Other times it shows up as automated triage, enrichment engines, or agent-based workflows layered onto existing SIEM infrastructure.
This ‘bolted-on’ AI interface only incrementally impacts the use, not the outcomes. What has not changed is the execution model. Detection is still constrained by the same indexes, the same static correlation logic, and the same alert-first workflows. Context is still assembled late, per incident, and largely by humans. Reasoning still begins after an alert has fired, not continuously as data flows through the environment.
This distinction matters because modern attacks do not unfold as isolated alerts. They span identity, cloud, SaaS, and endpoint domains, unfold over time, and exploit relationships that traditional SOC architectures do not model explicitly. When execution remains alert-driven and post-hoc, AI improvements only accelerate what happens after something is already detected.
In practice, this means the SOC gets better explanations of the same alerts, not better detection. Coverage gaps persist. Blind spots remain. The system is still optimized for investigation, not for identifying attack paths as they emerge.
That gap between perception and reality looks like this:
Each gap above traces back to the same root cause: intelligence added at the surface, while telemetry, correlation, and reasoning remain constrained by legacy SOC architecture.
Why Most AI SOC Initiatives Fail
Across environments, the same failure modes appear repeatedly.
1. Data chaos collapses detection before it starts Enterprises generate terabytes of telemetry daily, but cost and normalization complexity force selective ingestion. Cloud, SaaS, and identity logs are often sampled or excluded entirely. When attackers operate primarily in these planes, detection gaps are baked in by design. Downstream AI cannot recover coverage that was never ingested.
2. Single-mode retrieval cannot surface modern attack paths Traditional SIEMs rely on exact-match queries over indexed fields. This model cannot detect behavioral anomalies, privilege escalation chains, or multi-stage attacks spanning identity, cloud, and SaaS systems. Effective detection requires sparse search, semantic similarity, and relationship traversal. Most SOC architectures support only one.
3. Autonomous agents without governance introduce new risk Agents capable of querying systems and triggering actions will eventually make incorrect inferences. Without evidence grounding, confidence thresholds, scoped tool access, and auditability, autonomy becomes operational risk. Governance is not optional infrastructure; it is required for safe automation.
4. Identity remains a blind spot in cloud-first environments Despite being the primary attack surface, identity telemetry is often treated as enrichment rather than a first-class signal. OAuth abuse, service principals, MFA bypass, and cross-tenant privilege escalation rarely trigger traditional endpoint or network detections. Without identity-specific analysis, modern attacks blend in as legitimate access.
5. Detection engineering does not scale manually Most environments already process enough telemetry to support far higher ATT&CK coverage than they achieve today. The constraint is human effort. Writing, testing, and maintaining thousands of rules across hundreds of log types does not scale in dynamic cloud environments. Coverage gaps persist because the workload exceeds human capacity.
The Six Layers That Actually Work
A functional AI-native SOC is not assembled from features. It is built as an integrated system with clear dependency ordering.
Layer 1: Unified telemetry pipeline Telemetry from cloud, SaaS, identity, endpoint, and network sources is collected once, normalized using open schemas, enriched with context, and governed in flight. Volume reduction and entity resolution happen before storage or analysis. This layer determines what the SOC can ever see.
Layer 2: Hybrid retrieval architecture The system supports three retrieval modes simultaneously: sparse indexes for deterministic queries, vector search for behavioral similarity, and graph traversal for relationship analysis. This enables detection of patterns that exact-match search alone cannot surface.
Layer 3: AI reasoning fabric Reasoning applies temporal analysis, evidence grounding, and confidence scoring to retrieved data. Every conclusion is traceable to specific telemetry. This constrains hallucination and makes AI output operationally usable.
Layer 4: Multi-agent system Domain-specialized agents operate across identity, cloud, SaaS, endpoint, detection engineering, incident response, and threat intelligence. Each agent investigates within its domain while sharing context across the system. Analysis occurs in parallel rather than through sequential handoffs.
Layer 5: Unified case memory Context persists across investigations. Signals detected hours or days apart are automatically linked. Multi-stage attacks no longer rely on analysts remembering prior activity across tools and shifts.
Layer 6: Zero-trust governance Policies constrain data access, reasoning scope, and permitted actions. Autonomous decisions are logged, auditable, and subject to approval based on impact. Autonomy exists, but never without control.
Miss any layer, or implement them out of order, and the system degrades quickly.
Outcomes When the Architecture Is Correct
When the six layers operate together, the impact is structural rather than cosmetic:
Faster time to detection Detection shifts from alert-triggered investigation to continuous, machine-speed reasoning across telemetry streams. This is the only way to contend with adversaries operating on minute-level timelines.
Improved analyst automation L1 and L2 workflows can be substantially automated, as agents handle triage, enrichment, correlation, and evidence gathering. Analysts spend more time validating conclusions and shaping detection logic, less time stitching data together.
Broader and more consistent ATT&CK coverage Detection engineering moves from manual rule authoring to agent-assisted mapping of telemetry against ATT&CK techniques, highlighting gaps and proposing new detections as environments change.
Lower false-positive burden Evidence grounding, confidence scoring, and cross-domain correlation reduce alert volume without suppressing signal, improving analyst trust in what reaches them.
The shift from reactive triage to proactive threat discovery becomes possible only when architectural bottlenecks like fragmented data, late context, and human-paced correlation, are removed from the system.
Stop Retrofitting AI Onto Broken Architecture
Most teams approach AI SOC transformation backward. They layer new intelligence onto existing SIEM workflows and expect better outcomes, without changing the architecture that constrains how detection, correlation, and response actually function.
The dependency chain is unforgiving. Without unified telemetry, detection operates on partial visibility. Without cross-domain correlation, attack paths remain fragmented. Without continuous reasoning, analysis begins only after alerts fire. And without governance, autonomy introduces risk rather than reducing it.
Agentic SOC architectures are expected to standardize across enterprises within the next one to two years (Omdia, 2025). The question is not whether SOCs become AI-native, but whether teams build deliberately from the foundation up — or spend the next three years patching broken architecture while attackers continue to exploit the same coverage gaps and response delays.
When identity breaches cost an average of $4.8 million and 84% of organizations report direct business impact from credential attacks, you'd expect AI-powered security tools to be the answer.
Instead, security leaders are discovering that their shiny new AI copilots:
Miss obvious attack chains because user IDs don't match across systems
Generate confident-sounding analysis based on incomplete information
Can't answer simple questions like "show me everything this user touched in the last 24 hours"
The problem isn't artificial intelligence. It's artificial data quality.
Watch an Attack Disappear in Your Data
Here's a scenario that plays out daily in enterprise SOCs:
Attacker compromises credentials via phishing
Logs into cloud console → CloudTrail records arn:aws:iam::123456:user/jsmith
Exfiltrates via collaboration tool → Slack logs U04ABCD1234
Five steps. One attacker. One victim.
Your SIEM sees five unrelated events. Your AI sees five unrelated events. Your analysts see five separate tickets. The attacker sees one smooth path to your data.
This is the identity stitching problem—and it's why your AI can't trace attack paths that a human adversary navigates effortlessly.
Why Your Security Data Is Working Against You
Modern enterprises run on 30+ security tools. Here's the brutal math:
Enterprise SIEMs process an average of 24,000 unique log sources
Those same SIEMs have detection coverage for just 21% of MITRE ATT&CK techniques
Organizations ingest less than 15% of available security telemetry due to cost
More data. Less coverage. Higher costs.
This isn't a vendor problem. It's an architecture problem—and throwing more budget at it makes it worse.
Why Traditional Approaches Keep Failing
Approach 1: "We'll normalize it in the SIEM"
Reality: You're paying detection-tier pricing to do data engineering work. Custom parsers break when vendors change formats. Schema drift creates silent failures. Your analysts become parser maintenance engineers instead of threat hunters.
Approach 2: "We'll enrich at query time"
Reality: Queries become complex, slow, and expensive. Real-time detection suffers because correlation happens after the fact. Historical investigations become archaeology projects where analysts spend 60% of their time just finding relevant data.
Approach 3: "We'll train the AI on our data patterns"
Reality: You're training the AI to work around your data problems instead of fixing them. Every new data source requires retraining. The AI learns your inconsistencies and confidently reproduces them. Garbage in, articulate garbage out.
None of these approaches solve the root cause: your data is fragmented before it ever reaches your analytics.
The Foundation That Makes Everything Else Work
The organizations seeing real results from AI security investments share one thing: they fixed the data layer first.
Not by adding more tools. By adding a unification layer between their sources and their analytics—a security data pipeline that:
1. Collects everything once Cloud logs, identity events, SaaS activity, endpoint telemetry—without custom integration work for each source. Pull-based for APIs, push-based for streaming, snapshot-based for inventories. Built-in resilience handles the reliability nightmares so your team doesn't.
2. Translates to a common language So jsmith in Active Directory, jsmith@company.com in Azure, John Smith in Salesforce, and U04ABCD1234 in Slack all resolve to the same verified identity—automatically, at ingestion, not at query time.
3. Routes by value, not by volume High-fidelity security signals go to real-time detection. Compliance logs go to cost-effective storage. Noise gets filtered before it costs you money. Your SIEM becomes a detection engine, not an expensive data warehouse.
4. Preserves context for investigation The relationships between who, what, when, and where that investigations actually need—maintained from source to analyst to AI.
What This Looks Like in Practice
The 70% reduction in SIEM-bound data isn't about losing visibility—it's about not paying detection-tier pricing for compliance-tier logs.
More importantly: when your AI says "this user accessed these resources from this location," you can trust it—because every data point resolves to the same verified identity.
The Strategic Question for Security Leaders
Every organization will eventually build AI into their security operations. The question is whether that AI will be working with unified, trustworthy data—or fighting the same fragmentation that's already limiting your human analysts.
The SOC of the future isn't defined by which AI you choose. It's defined by whether your data architecture can support any AI you choose.
Questions to Ask Before Your Next Security Investment
Before you sign another security contract, ask these questions:
For your current stack:
"Can we trace a single identity across cloud, SaaS, and endpoint in under 60 seconds?"
"How long does it take to onboard a new log source end-to-end?"
For prospective vendors:
"Do you normalize to open standards like OCSF, or proprietary schemas?"
"How do you handle entity resolution across identity providers?"
"What routing flexibility do we have for cost optimization?"
"Does this add to our data fragmentation, or help resolve it?"
If your team hesitates on the first set, or vendors look confused by the second—you've found your actual problem.
The foundation comes first. Everything else follows.
Stay tuned to the next article on recommendations for architecture of the AI-enabled SOC
What's your experience? Are your AI security tools delivering on their promise, or hitting data quality walls? I'd love to hear what's working (or not) in the comments.
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“It's amazing that a data pipeline tool can do this level of pre-processing to filter out irrelevant data and produce insights."
Ricky Allen
,
Chief Technology Officer
|
CyberOne Security
We have recently started a journey with DataBahn and I can’t speak highly enough about the product or the amazing team at Databahn.
Greg Stewart
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Senior Director, Cybersecurity
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CSL Behring
I was lucky enough to get a demo of DataBahn and was blown away at the capabilities and the impact the platform will deliver.
Keith Schlosser
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Group CIO
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AXIS Capital
"We reduced 70% of our data going to our SIEM. And here’s the game-changer: no ingress, egress, or API fees."
Abraham Selvaraj
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Director, Information Security
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ThinkOn
While DataBahn.ai is a perfect use case for SIEM solutions like Sentinel, I believe its use case is even broader as the "Data Pump" for all enterprise data.
Michael Keithley
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Member, Board of Directors
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Fractional CIO/CTO, Former CIO/CTO at CAA & UTA
"Databahn’s approach has truly simplified Sentinel, making it more efficient and cost-effective."
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