From Visibility to Autonomous Defense:  Why Security Operations In Saudi Arabia Must Evolve Now

By: Eleftherios Antoniades, Founder & CTO ClearSkies

For the last two decades, our approach to cybersecurity has been fundamentally reactive. We operated on a simple assumption: If we can see the threat, we can stop it.

Today, as organizations across Saudi Arabia accelerate digital transformation and expand connected environments, the limitations of this reactive model are becoming increasingly visible.

The traditional operating model is reaching its maximum structural capabilities – not because security teams aren’t capable, but because the modern attacks have increased in velocity, innovation and automation, outpacing human-scale response.

Cyberspace now operates at a velocity that no human team, however experienced, can track. The vast majority of attacks are now fully automated, with latest data suggested 14% of all cyberattacks are fully automated with no human intervention. Approximately 40% are driven by AI, inherently unpredictable, and completely incessant.

Reports suggest that in the Kingdom, millions of cyber threats are carried out that “likely include automated components.”

The key question is no longer if an attack will happen, but when. And yet, paradoxically, we often find ourselves trapped in a perpetually reactive security model, a posture that, by definition, guarantees we will always remain one critical step behind.

In the Kingdom, this challenge is especially relevant for regulated and high-impact sectors – from financial services and healthcare to government entities, critical infrastructure and large enterprises – where resilience, governance and response speed are directly tied to operational continuity and trust.

In today’s era, the Security Operations Center (SecOps) has ceased to be a strategic asset. It has transformed into an overwhelming cost center, an unavoidable bottleneck that is unable to scale effectively to keep pace with the unprecedented speed and exponential volume of automated, AI-driven attacks.

We are drowning in data, burning out our most valuable talent, and measuring success by the speed of alert and incident management, not by the breaches we actively prevent.

Here we outline the strategic evolution of the Security Operations Center (SecOps) from its current state to a future-proof, autonomous defense. This is not just a technological upgrade; it is a fundamental shift in our operational model. We will move from:

1. SIEM (Visibility): “Help me find the needle in the haystack.”

2. TDIR (Workflow): “Help me find the needle faster.”

3. The Autonomous SecOps (Action): “The machine finds the needle, analyzes it, and neutralizes the threat before it can even manifest.”

This transition, powered by Generative-AI (the “brain”) and Agentic AI (the “hands”), will transform the SecOps from a resource-draining cost center into an autonomous, scalable, and effective operational agent that delivers continuous value.

The primary business driver is no longer just risk reduction; it is operational resilience and competitive advantage in an environment that evolves at light speed.

The Inefficient Central Log Observatory: The Era of SIEM and Alert Fatigue

The first generation of the modern SecOps was built on Security Information and Event Management (SIEM). The business logic was simple: “We can’t stop what we can’t see.” We invested, and continue to invest, significant sums to centralize logs from every IT, security, network, Internet of Things (IoT), and Operational Technology (OT) system into a central “digital watchtower.”

This achieved its primary goal: Visibility.

For the first time, we had a “single pane of glass” for basic threat detection and compliance checks. The Business Problem: We succeeded in building a central log observatory that produces ten thousand alarms for every one intruder, drowning the real threats in a chaos of false positives.

But what began as a “Window of Visibility” turned into a “Central Log Observatory of Noise.”

This model created a new, unsustainable liability:

• Skyrocketing Operational Expenditures (OpEx): We were forced to hire armies of Tier 1 and 2 analysts to perform repetitive, low-value work: manually managing a flood of false positives.
• Burnout: We took highly skilled (and highly paid) security engineers and turned them into “alert watchers”, leading to massive burnout and attrition in one of the most critical talent pools.
• Slow Response: The Mean Time to Respond (MTTR) was measured in days or even weeks. By the time an analyst manually pieced together the evidence, the intruder had, in most cases, already achieved their goal.

The SIEM-only model has reached its limit. It is a high-cost, high-friction, low-efficiency system that scales in the most expensive way: linear. Every increase in data requires more analysts, more resources, more tools, creating a chain of dependency that constantly burdens the budget without a corresponding increase in effectiveness.

The result? A system that works harder but protects less.

The Optimized Assembly Line: The Rise of TDIR Platforms

The next phase in the evolution of SIEM is Threat Detection, Investigation, and Response (TDIR) platforms. This technology recognized the limits and weaknesses of traditional SIEM and added new tools, primarily Automation Playbooks / SOAR, Identity Threat Protection (ITP), a Root Cause Analysis Engine, and Zero-Trust & Data Sovereignty.

If SIEM was the “central log observatory” TDIR is the “assembly line.” We don’t just watch the alerts; we build a process for them.

We created automated “playbooks” to handle the most common, repetitive tasks. For example, when a “phishing” email was reported, a SOAR playbook could automatically check the email’s links against a threat database and at the same time delete similar emails from other users’ inboxes.

This was a significant improvement. TDIR optimized the existing model, reducing analyst work and cutting down the Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) from days to hours for known and repetitive threats.

The Business Problem: The “Assembly Line” still requires a human operator at every critical step

TDIR optimized the manual SecOps; it did not replace it. An analyst is still required to investigate any new threat, correlate complex events, and make the final “yes/no” decision for a critical response (e.g., “Should we shut down this executive’s laptop?”).

We made the “Assembly Line” faster, but the human “bottleneck” remained. In the face of machine-speed attacks, a human-in-the-loop workflow, no matter how optimized, is a guaranteed defeat.

The Self-Defending Enterprise: The Autonomous SecOps

We are at the inflection point of the third and most decisive evolution: the Autonomous SecOps.

This model changes the basic assumption. Instead of using technology to help the human, it uses technology to replace machine-scale tasks, freeing humans to focus exclusively on high-level strategic threat hunting and exception handling. This evolution must be pursued responsibly – with clear governance, transparency and human oversight built into the operating model.

The Autonomous SecOps relies on a “new workforce”: a collaboration of two types of Artificial Intelligence (AI) that work in parallel, composing a new ecosystem for detecting and responding to targeted, automated and AI-driven attacks:

1. Generative AI: The “Analyst Brain”

Generative-AI (like the models that power ChatGPT, Gemini etc.) functions as the cognitive engine of the SecOps. It reads, understands, and synthesizes data that no human team ever could.

• Its Job: To investigate and report.
• The Business Value: It replaces the investigation and triage function. Instead of 50,000 alerts, a human analyst receives a single, substantive one-paragraph summary from the Generative AI, for example:

“I have investigated 47,522 alerts in the last hour, which I have correlated into a single high-priority incident. A user in the Finance Department (Odysseas Odysseos) clicked a phishing link, which installed malware. The malware is now attempting to move laterally to the finance server. I have assessed this as a “Critical Threat, Recommend immediate containment.”

“Think of Generative-AI as the ‘brain’ that finally solves the “noise” problem. It ends burnout from thousands of irrelevant alerts and endless triage, turning data volume into clear, strategic insight.

But strategic insight alone doesn’t stop an attack. A “brain” that just suggests is not enough. This is where Agentic AI comes in. It is the “hands” the autonomous executive force that takes action instantly, turning decision into immediate, defensive outcomes.”

2. Agentic AI: The “Operator Hands”

This is the real game-changer: If Generative-AI is the “brain” that creates the plan, Agentic AI is the “hands” that executes it.

An AI Agent is an autonomous entity that is given a goal, a set of tools (APIs), and the authority to act without needing human-in-the-loop approval for its tasks.

• Its Job: To perceive, evaluate, and act.
• The Business Value: It autonomously executes the entire response workflow within seconds.

Continuing our example, the moment the Generative-AI identifies the threat, it assigns the goal (“Contain this threat”) to the AI-Agents. The agents then coordinate to:

1. Isolate: An agent immediately quarantines Odysseas Odysseo’s laptop from the network.
2. Block: A second agent pushes a rule to all Firewalls to block the malicious IP address.
3. Revoke: A third agent communicates with the identity protection systems to revoke the user’s credentials, cutting off the attacker’s access.
4. Report: The Generative-AI agent creates a new “Incident Ticket”, populates it with all actions taken, and notifies the human SecOps team of the resolved incident.

We are transforming response time from days to seconds. The entire incident lifecycle, from initial detection to full remediation, is now completed in under a minute. In practice, the threat is neutralized before a human analyst can even read the initial alert

The Business Case for Autonomy: Why This Is Imperative for All C-Level Executives

This is not a technical roadmap; it is an operational survival plan. The benefits directly impact our key financial and operational indicators.

1. Breaking the Linear Cost Equation (OpEx): Until now, our defensive model was financially unsustainable: every increase in threats inevitably led to a corresponding, linear increase in operational costs (OpEx) for personnel and tools. The autonomous model completely reverses this equation. It transforms cybersecurity from an uncontrolled, variable cost center into a predictable, strategic investment. Our ability to handle 10 million alerts no longer costs more than handling 200. We definitively decouple threat volume from our budget, achieving limitless defensive scaling at rather fixed cost.
2. Talent Optimization & Addressing the Shortage: Our strategy can no longer be based on finding more experts. The global cybersecurity talent gap is a given. Autonomy is the solution: It transforms our elite analysts from “alert managers” into strategic “threat hunters”. By freeing them from repetitive, burnout-inducing tasks, we maximize our return on investment (ROI), allowing them to focus exclusively on strategic cyber defense and addressing unknown, complex threats.
3. Annihilating Risk Through Speed (Resilience): The true cost of a breach (financial and reputational) is determined by one factor: the attacker’s “dwell time” in our network. The transition to an autonomous response doesn’t just reduce the Mean Time to Respond (MTTR) from days to seconds, it fundamentally changes the outcome: we are no longer managing the crisis but preventing it before it even manifests. This is the most effective strategy to drastically minimize the impact we would suffer from a successful attack/intrusion.
4. Defense at Machine Speed: Our adversaries are already leveraging AI to automate and scale their attacks. They operate at machine speed. It is strategically impossible to win this fight by relying on human response speed. Adopting the Autonomous SecOps is not just an upgrade; it is the only viable strategy to level the playing field and ensure our defensive adequacy.

As a C-Level Executives, our responsibility is to recognize that investing in the current, reactive security model is not just unsustainable, it is now a direct risk to our very operational resilience.

We must promptly change our strategy course, moving our resources from simple incident management (a tactic that merely documents the damage) to building a truly autonomous defensive system that ensures uninterrupted operational continuity, thus resilience.

This change also requires a new way of measuring performance. The critical Key Performance Indicator (KPI) we set for our security leaders is no longer “How many thousands of alerts did we close?”.

For Saudi organizations building resilience at scale, the strategic question is no longer only “how many alerts did we close?”, but how quickly and safely we can neutralize threats while maintaining continuity and trust.