Legacy systems and governance models undermine AI effectiveness in SOCs
If you’re serious about AI in cybersecurity, legacy systems must go. They were built for a different time, slower threats, fewer endpoints, less data. Today, attackers break through enterprise defenses in 51 seconds, according to CrowdStrike’s 2025 Global Threat Report. That makes legacy governance pace, quarterly reviews, fragmented tool chains, obsolete.
Companies like Carvana, Salesforce, and the City of Las Vegas are already showing what’s possible. They’ve pulled ahead not because they’re using radically different AI, but because they made the decision to remove internal barriers. AI is only as smart as the system it operates within. And it can’t function effectively inside a security operation center (SOC) that’s using processes designed for humans working at human speed.
The issue is that many organizations are still clinging to outdated infrastructure, fearing disruption more than they fear breach. That’s not rational. Over 70% of enterprises experienced at least one AI-related breach in the past year. Still, too many teams treat AI as a plugin, expecting it to bring transformational results without rethinking governance or tool architecture.
The CISO’s job isn’t just technical anymore. It’s strategic. If AI is deployed inside old guardrails, you’ll limit your response time, visibility, and impact. The tools and governance models need to operate at the speed of machine-scale decision-making, because that’s what’s coming. Ignoring that reality won’t stop it.
As Allie Mellen, Principal Analyst, put it: the real chaos agent isn’t the attackers, it’s generative AI itself. But that chaos can be harnessed. If you take ownership early and remove friction points, AI can shift from theoretical value to tangible advantage.
George Kurtz, CEO of CrowdStrike, was direct in his keynote: “The legacy SOC can’t compete.” He’s right. Rebuilding your foundation isn’t just smart, it’s necessary.
Organizational fragmentation drives AI agent failures
AI isn’t failing because the tech doesn’t work. It’s failing because most organizations aren’t ready for it. The issue isn’t the algorithm. It’s the environment the algorithm is dropped into. Fragmented, siloed, legacy systems don’t support the real-time, data-hungry functions AI depends on.
Carnegie Mellon’s AgentCompany benchmark shows AI agents fail 70% to 90% of the time on complex enterprise tasks. That looks bad, until you ask why. Salesforce’s internal testing found AI agents fail over 90% of the time when traditional security guardrails are applied. But here’s the paradox: 79% of executives report meaningful productivity gains from AI agents already in deployment. So what’s happening?
It’s simple: the ones seeing results have addressed the structure first. They’ve built the operational architecture to support AI effectively. The rest are throwing modern tech into old workflows. That’s why it’s crashing.
In most enterprises, security stacks sprawl across dozens of tools and vendors. The average is 83 tools from 29 vendors. That kind of fragmentation kills context. Each tool generates its own data stream, often disconnected from the others. So when AI tries to correlate that data in real time, the output suffers. False positives spike. Accuracy plummets.
Executives shouldn’t get tangled in tech minutiae. Focus on decisions that matter. You can’t scale AI within a broken system. Before automating detection and response, integrate your toolsets. Unify your telemetry. That’s where the lift is. It’s not glamorous work, but it makes all the difference. Strip out what’s redundant. Centralize what matters. Let AI operate with full visibility and clean, connected inputs.
AI doesn’t fail, environments do. Fix the foundation, and the tech delivers.
Achieving machine-speed governance requires a fundamental architectural overhaul
AI doesn’t wait. It runs on machine time, millions of decisions per second. Governance, on the other hand, still follows a playbook built for manual control: daily approvals, monthly audits, quarterly reviews. That mismatch isn’t just inefficient, it’s dangerous. It paralyzes the system right when speed matters most.
If decision-makers want to unlock AI’s full performance within cybersecurity, they need architecture that moves at AI’s pace. That means moving beyond piecemeal oversight toward unified, automated governance built into the platform itself.
CrowdStrike, SentinelOne, Palo Alto Networks, and Trellix are designing with this in mind. Their platforms don’t just plug in AI, they consolidate endpoint, identity, cloud, and threat streams into a single telemetry pipeline. This gives security operations the input they need to make snap decisions without sacrificing control.
The benefits are immediate and measurable. Policy enforcement moves from static documents to real-time code. Guardrails, like data residency rules, privileged access, and usage limits, are set once and executed consistently across environments. Investigations and audit trails are pulled from a single source of truth. There’s no manual patchwork, no divergent logs, no unreliable signals.
CrowdStrike’s Falcon platform is one clear example. It enables continuous control monitoring, where every policy, across identity, workload, endpoint, is proven effective live, not sampled occasionally. Enforcement loops are closed automatically. If an AI agent crosses a threshold, compensating controls kick in, revoking credentials, isolating a process, on the spot.
This kind of architecture doesn’t just support AI governance. It’s the requirement. You need fast, precise enforcement, governed by a single identity model and a unified view of operational truth. Without that, scaling AI across your SOC will only amplify risk. With it, governance stops bottlenecking innovation and starts enabling it.
Tool sprawl and fragmented data pipelines diminish AI security performance
Too many tools kill clarity. The average enterprise runs 83 security solutions across 29 vendors, each working with its own data stream, format, and access controls. AI thrives on consistent, connected input. When that’s missing, performance suffers.
In real-world application, tool sprawl creates a flood of false positives. Some organizations see false alert rates over 30%. That level of noise overwhelms teams and undermines trust in AI-driven decisions. When security analysts are forced to chase unreliable alerts, response times drag, and critical threats hide in plain sight.
Fragmented data pipelines are the root of the problem. Each tool may perform well in isolation, but AI agents can’t stitch these isolated signals into one coherent picture. It’s not just inefficiency, it’s a direct hit to detection accuracy.
74% of enterprises already operate in multi-vendor ecosystems. That’s not going away. But it’s not an excuse to tolerate integration failure. 43% of those same companies say lack of cross-platform integration is a top operational burden. If AI is going to deliver meaningful value, those gaps have to close.
What’s needed is architectural consolidation, not necessarily fewer vendors, but better interoperability. AI doesn’t need more data; it needs better access to the right data. That only happens when your systems are designed to talk to each other natively, in real-time.
For executives, the takeaway is simple. Audit your stack. Not every tool adds value. Remove what doesn’t. Prioritize platforms that aggregate telemetry natively. Standardize data formats. Use APIs strategically. Every piece of noise you eliminate gives AI more signal.
When your architecture is fragmented, AI can’t operate effectively. When your data streams are aligned, performance shifts fast, and decisively. That’s when AI starts delivering real-time insight, not just output.
CISOs must transform from gatekeepers to strategic enablers
The role of the CISO is changing, fast. This isn’t just about defending systems anymore. It’s about driving growth and aligning security with business outcomes. The CISOs who are winning today are those who’ve made the shift from controller to enabler, embedding AI, automation, and integrated decision-making into every part of the business.
A CISO’s influence isn’t limited to compliance or response. It reaches product velocity, user trust, and even revenue creation. Teams that once focused exclusively on access controls now help accelerate launches by replacing manual checkpoints with automated guardrails. This is where value comes from. Automation at machine speed doesn’t just keep up, it unlocks entirely new momentum across the company.
Pritesh Parekh, CISO at PagerDuty, said it clearly: “When security is done right, we’re actually accelerating the business by eliminating manual checkpoints and replacing them with automated guardrails.” This mindset is becoming standard among high-performing security teams.
The shift is not abstract, it’s measurable. Organizations with integrated security and IT operations report 30% fewer major incidents than those still stuck in organizational silos. These are not theoretical gains. Fewer incidents means fewer breaches, less downtime, and stronger reputational coverage at the board level.
This is what strategic security leadership looks like now. Andrew Obadiaru, CISO at Cobalt, emphasized that execution across security and AI must simply be better moving forward. He’s not wrong. The pace of technological acceleration doesn’t slow down for governance gaps, cultural silos, or operational inertia.
For results that matter, CISOs need to break out of reactive functions and step into roles that shape how the business grows. That means building security architectures that scale with AI, collapsing manual workflows, and embedding security decisions into business systems that self-correct without delay.
One CISO from a financial services firm told VentureBeat that tying his team’s performance to new revenue was “the single best decision” he made. That framing resonates. Growth-enabling security is measurable, repeatable, and defensible, in boardrooms, in audits, and with customers.
Now’s the time to lead decisively. Don’t wait to be asked. Secure the business at the speed it moves. When done right, security doesn’t slow down innovation, it makes it unstoppable.
Main highlights
- Remove legacy friction to unlock AI in the SOC: AI underperforms not because of its capabilities, but due to legacy processes and disconnected tools that choke its potential. Leaders should prioritize dismantling outdated systems that can’t match the speed or precision AI requires.
- Eliminate fragmentation before scaling AI: AI agent failures often stem from fragmented data and tool stacks, not technical shortcomings. Decision-makers should streamline environments and unify telemetry to improve AI outcomes and reduce time-wasting false positives.
- Modernize governance to match machine speed: Traditional security governance is too slow for AI’s real-time demands. Executives must adopt platform-level architecture that allows for continuous monitoring, automated enforcement, and scalable policy control.
- Consolidate tools to reduce signal noise: Tool sprawl creates data silos that disrupt AI performance and amplify alert fatigue. CISOs should reduce tool count and prioritize platforms that offer native integration and unified data streams.
- Reposition CISOs as business accelerators: Security leadership must evolve from risk gatekeeping into enabling strategic outcomes. Organizations that integrate security with business and IT operations report fewer incidents and create measurable business value through faster, safer execution.
