From Siloed Alerts to Unified Action: Moving Beyond Detection-Centric Cybersecurity

An attacker who gains access through a stolen credential may quickly move across devices, cloud resources and network systems in search of valuable data or greater control. Each step can generate clues, but when those signals land in separate tools and queues, security teams may struggle to recognize the full attack before the damage spreads.

This challenge is becoming harder to tackle as traditional boundaries between users, devices, applications and infrastructure continue to dissolve. NIST’s zero-trust guidance reflects that shift, moving security away from static, network-based perimeters and toward continuous decisions based on users, assets and resources. This same erosion of boundaries is what makes it harder to catch attackers who don’t need to break anything to move around. CISA’s guidance on identifying and mitigating “living off the land” techniques warns that attackers can abuse legitimate, trusted tools and processes to blend in with normal system activity, making isolated alerts harder to interpret without broader context.

Yet many organizations still measure security effectiveness largely by how well they detect suspicious activity. Detection remains essential, but alerts alone don’t determine which risks matter most, coordinate action across environments, or help the business continue operating when defenses fail. That requires an approach that connects visibility with timely decisions, enforceable controls and plans for maintaining and restoring critical operations.

Moving beyond a detection-centric model means reconsidering how security data, decisions and defenses work together across the enterprise. Below, members of the Senior Executive Cybersecurity Think Tank—with expertise in enterprise cybersecurity strategies, data breach prevention, risk management, threat detection and cybersecurity leadership—explain what a more unified, real-time approach to visibility, control and resilience should look like.

“Organizations need a unified layer that correlates identity, endpoint, cloud and network signals in real time, shifting from alerts to measurable outcomes.”

– Bhavya Bhandari, Cybersecurity Risk Management Leader, Financial Services at Ernst & Young US LLP

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Connect Security Signals to Business Outcomes

Bhavya Bhandari, Cybersecurity Risk Management Leader, Financial Services at Ernst & Young US LLP, says security functions can no longer stay tool-centric or reactive.

“Organizations need a unified layer that correlates identity, endpoint, cloud and network signals in real time, shifting from alerts to measurable outcomes,” he says.

For Bhandari, that evolution also changes how organizations should approach detection and response.

“In the current threat landscape, we see the emphasis shifting to reducing the time to detect and respond,” he says. “This can only be realized if continuous visibility, automated response and resilience are built into operations, not bolted on after detection.”

Prepare to Respond at AI Speed

Ken Grohe, President of  LeverageGTM Inc., says AI is compressing the time organizations have to recognize and control cyber incidents.

“AI-driven cyberattacks are increasing to no-latency speeds, collapsing organizations’ response windows from days to seconds,” he says. “Most still remain unprepared to control these incidents comprehensively.”

Grohe argues that this shift calls for a new approach to incident response.

“We need a new first line of defense,” he says. “The category of cyber incident response management will be challenged to create an agentic platform that enables real-time, cross-functional response at AI speeds.”

Reduce Attack Paths and Limit the Blast Radius

David Etue, CEO of Cyberbit, says organizations need defenses that account for how attackers move across multiple parts of the technology environment.

“Security teams and tools can’t operate in silos when attacks now span identities, endpoints, cloud and networks,” he says. “Moving beyond detection-centric security means complementing it with unified exposure management, identity-centric controls and resilient architectures that reduce exploitable pathways and limit blast radius when incidents occur.”

Detection and response still play a critical role, Etue says, but they must be supported by broader context and coordinated action.

“Detection and response remain essential as adversaries innovate and software fails,” he says. “A prepared security operations team powered by a unified control plane and telemetry normalized to MITRE ATT&CK can see attack paths earlier, prioritize what truly matters to the business, automate containment, validate control effectiveness, and measurably improve defensibility and operational readiness.”

Connect Visibility, Ownership and Response

Harikrishnan Muthukrishnan, Principal IT Developer for BCBS FLORIDA, says organizations need to move beyond detection-centric security by connecting visibility, ownership and response into one operating model.

“Real-time visibility starts with a common asset inventory across users, devices, cloud workloads, APIs, data stores and third-party connections, tied to business ownership and criticality,” he explains. “You cannot protect what you cannot identify or assign accountability for.”

From there, Muthukrishnan says, controls must become adaptive rather than static.

“A normal action may be allowed at low risk, challenged at moderate risk, and blocked when identity, endpoint, cloud or network signals show elevated risk,” he says.

Muthukrishnan adds that automation should extend beyond sending alerts.

“Automation should be used for containment, not just notification: Revoke suspicious sessions, disable tokens, isolate endpoints, block cloud keys, quarantine workloads and restrict data movement, while analysts focus on judgment-heavy decisions.”

“Physics guarantees latency will never be zero, but attackers are bound by the same constraints. Make your signal propagation faster than their lateral movement.”

– Nirwan Dogra, Senior Software Engineer at Microsoft

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Transmit Signals Faster Than Attackers Can Move

Nirwan Dogra, Senior Software Engineer at Microsoft, frames the challenge as one of coordination and speed.

“Most security stacks do a fine job of detecting threats,” he says. “They just can’t talk to each other fast enough to stop one. An attacker moves from identity to endpoint to cloud, and four tools see something, but none act together in time.”

Dogra says closing that gap requires organizations to consider how quickly useful information moves between security systems.

“This is a distributed systems problem—moving signals between systems at low enough latency to act before the attacker completes their chain,” he says. “It requires smart compression, selective routing and sending the right context to the right enforcement point in milliseconds.”

Although some delay is unavoidable, Dogra says organizations can still build systems that operate faster than an attacker can advance.

“Physics guarantees latency will never be zero, but attackers are bound by the same constraints,” he says. “Make your signal propagation faster than their lateral movement. Machine learning compresses noise into actionable context targeted at specific threats. Resilience isn’t better dashboards; it’s infrastructure that outpaces the attacker at every hop.”

See the Attack Surface Through an Adversary’s Eyes

Kumar Ritesh, Founder, Chairman and CEO of CYFIRMA, says disconnected security systems can leave critical gaps in an organization’s defenses.

“Siloed tools create siloed vision, and attackers exploit the gaps between them,” he says.

Ritesh argues that organizations must reconsider a model built primarily around identifying attacks after they begin.

“Detection-centric models assume you’ll catch threats after they enter. That assumption is broken,” he says. “Organizations must shift to preemptive cybersecurity correlating identity, endpoint, cloud, AI assets and network signals against real-time intelligence on who is targeting you, how and why.”

That approach changes the meaning of both visibility and resilience.

“Unified visibility means knowing your attack surface as adversaries see it before they act,” Ritesh says. “Resilience means your posture adapts continuously as threats evolve.”

The challenge, he says, isn’t necessarily that organizations lack security products.

“Organizations fail not from a lack of tools, but from a lack of connected intelligence,” Ritesh says. “The right model integrates threat actor profiling, vulnerability context and attack surface data into a single operational picture.”

He concludes by reframing the full context of the adjective “real-time.”

“‘Real-time’ isn’t just speed; it’s relevance,” Ritesh says. “A unified approach asks not just, ‘What happened?’ but, ‘What is being planned against us right now?’ and acts before the breach becomes the headline.”

Consolidate Security Data, Not Just Tools

Gaurav Kulkarni, Senior Manager, Cybersecurity Engineering for Staples, says security models designed around a defined perimeter no longer reflect how modern organizations operate.

“Detection-centric security made sense when the perimeter was defined,” he says. “That perimeter no longer exists, and tools designed to defend it are creating blind spots across identity, endpoints, cloud and network simultaneously.”

Kulkarni says organizations need to shift from tool consolidation to data consolidation.

“Unified visibility isn’t about buying a single platform; it’s about ensuring every signal, across every environment, feeds a common risk model in real time,” he says. “Siloed tools produce siloed decisions. One cannot respond to a cross-environment attack chain with a point-solution mindset.”

Kulkarni stresses that organizations also need to determine which response actions can be taken automatically.

“Real-time control means automated response that doesn’t wait for a human to connect the dots across three dashboards,” he says. “The organizations that get this right aren’t faster at detection—they’ve reduced the number of decisions that require human intervention in the first place.”

Understand the Full Attack Path

Maman Ibrahim, Founder of Ginkgo Resilience LTD, says security teams must look beyond individual warnings to see how an attack is developing.

“Organizations need to move from ‘find the alert’ to ‘understand the attack path,’” he says. “That means joining identity, endpoint, cloud, network and application signals into one operating view, then linking detection to prevention, response and recovery.”

Ibrahim says a unified, responsive model starts with shared telemetry, common asset ownership and risk-based correlation. 

“If a stolen identity touches a risky endpoint, reaches a cloud workload and triggers unusual data movement, the system should see the chain in real time, not as four tickets,” he says. “Controls should adapt quickly, stepping up authentication, isolating devices, blocking sessions, rotating secrets and updating playbooks.”

That coordination, he adds, is central to building resilience.

“Resilience comes when visibility, decision rights and automated response work as one muscle.”

“The goal isn’t more dashboards; it’s a living system that continuously discovers risk, enforces controls, adapts to change and provides complete traceability across human and machine actors.”

– Anand Salodkar, Co-Founder and COO of CompFly AI

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Build a Real-Time Governance Layer

Anand Salodkar, Co-Founder and COO of CompFly AI, says the speed and breadth of modern attacks are placing additional pressure on security models built primarily around detection.

“Detection-centric security assumes you’ll find the attack after it starts,” he says. “Modern attacks move across identities, cloud resources, endpoints, APIs and, increasingly, AI agents in seconds, making siloed tools insufficient.”

Salodkar says the next evolution is a unified control plane built around identity, context and real-time enforcement. 

“Organizations need continuous visibility into who (or what agent) is acting, what resources are being accessed, what decisions are being made, and whether those actions align with policy,” he says.

Resilience, Salodkar adds, comes from moving beyond alerts to automated prevention, containment and recovery. This strategy is especially important as AI agents assume a larger role in business processes.

“The goal isn’t more dashboards; it’s a living system that continuously discovers risk, enforces controls, adapts to change and provides complete traceability across human and machine actors,” he says. “In an agentic world, security must become a real-time governance layer, not just a monitoring function.”

Establishing a More Unified, Responsive Security Model

• Connect security signals to measurable business outcomes. Bring identity, endpoint, cloud and network data together so security teams can reduce detection and response times rather than simply generate more alerts.
• Build incident response capabilities that can operate at AI speed. Organizations need real-time, cross-functional systems that can coordinate action within seconds as AI-driven attacks compress traditional response windows.
• Reduce exploitable pathways before an incident occurs. Pair detection and response with exposure management, identity-focused controls and resilient architecture to limit both attack opportunities and potential blast radius.
• Create a common inventory with clear ownership. Maintain an up-to-date view of users, devices, workloads, APIs, data stores and third-party connections, then tie each asset to business criticality and accountability.
• Move security context faster than attackers can move laterally. Improve how signals are compressed, routed and delivered so the right enforcement point receives actionable information before an attack chain advances.
• Evaluate the attack surface from an adversary’s perspective. Combine threat intelligence, vulnerability context and attack surface data to anticipate how attackers may target the organization, not just document what has already happened.
• Consolidate security data rather than focusing only on tool consolidation. Feed signals from across the enterprise into a shared risk model so separate products can contribute to coordinated decisions and automated action.
• Track the full attack path instead of treating each alert as a separate event. Correlate activity across identities, endpoints, cloud environments, applications and networks so defenses can adapt as risk escalates.
• Extend real-time governance to human and machine actors. Continuously evaluate who or what is acting, which resources are being accessed, and whether those actions align with policy, particularly as AI agents take on more business tasks.

From More Alerts to Faster, Smarter Action

The next stage of enterprise security won’t be defined by how many alerts an organization can collect. It will depend on whether teams can combine signals across environments, understand the full attack path and translate that context into timely decisions, adaptive controls and automated containment.

As technology environments become more interconnected and AI agents become more active, fragmented security models will create even greater operational risk. When defenses are tested, shared visibility, clear ownership and coordinated response can not only stop attacks sooner but also maintain critical functions and support more effective recovery.