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How Agentless Asset and Vulnerability Discovery Works

Cyber systems grow fast. One day, you see 50 devices. The next day, you see 500. Laptops, servers, cloud apps, IoT tools, all connect at once. You lose track quickly. That’s where agentless asset and vulnerability discovery steps in. It gives you full visibility without installing anything on every device.

Let’s break it down in a simple way.

What Is Agentless Asset And Vulnerability Discovery?

Agentless asset and vulnerability discovery means finding and tracking devices, software, and security gaps without installing software agents on each system. Instead of “touching” every device, it listens, scans, and collects data from the network itself. That makes it fast, light, and easy to scale.

A report on IT asset discovery shows that agentless methods are now a key part of modern tools because they reduce setup effort and improve visibility across complex systems.

And this matters more than ever. Cloud use, remote work, and IoT devices keep growing. This growth pushes companies toward agentless asset and vulnerability discovery because manual tracking just cannot keep up.

Why Do Businesses Depend On It?

Companies want one thing: clear visibility. Without it, they miss devices. And missed devices mean hidden risks.

A market study shows the agentless discovery tool market may grow from about USD 1.34 billion in 2026 to USD 3.36 billion by 2035, driven by rising demand for automated IT visibility and security.

That growth tells a simple story. More complexity = more need for agentless asset and vulnerability discovery. It also shows something else. Security teams now prefer tools that work across cloud, on-prem, and hybrid systems without heavy installation.

How Agentless Asset And Vulnerability Discovery Works (Step-By-Step)

Now let’s walk through how it actually works in real systems.

Network scanning starts first

The system scans the network using protocols like:

SNMP

WMI

SSH

API calls

This scan helps identify active devices. At this stage, agentless asset and vulnerability discovery builds a raw map of everything connected.

Device identification happens

Once scanning starts, the system collects details like:

IP address

OS type

open ports

installed software

device role

This step builds a full asset inventory. So instead of guessing, agentless asset and vulnerability discovery creates a real-time list of all assets.

Vulnerability mapping begins

Now comes the important part. The system compares discovered assets with known vulnerability databases.

It checks:

outdated software versions

missing patches

exposed services

weak configurations

This is where agentless asset and vulnerability discovery turns into a security tool, not just an inventory tool.

Risk scoring and prioritization

Not every issue is equal. So the system ranks risks based on:

severity

exploitability

business impact

This helps security teams focus on what matters first. Without this step, agentless asset and vulnerability discovery would only give data, not direction.

Continuous monitoring

The network keeps changing. New devices appear daily. So the system keeps scanning in cycles. This continuous loop keeps agentless asset and vulnerability discovery updated in real time.

Why Agentless Matters More Today?

Modern IT systems are messy. You don’t just manage servers anymore. You manage:

cloud workloads

remote employees

SaaS apps

IoT sensors

A security industry report shows global security services revenue reached about $77.1 billion in 2024, growing nearly 9.9% year-over-year, driven by rising cyber complexity.

That growth connects directly to one need: better visibility. And that’s exactly what agentless asset and vulnerability discovery solves.

Agentless Vs Agent-Based Discovery - Key Advantages

Here’s why teams like it:

No installation hassle

You don’t touch every device. That saves time and effort.

Faster visibility

You get a full picture quickly.

Works in mixed environments

Cloud + on-prem + hybrid all work together.

Better compliance support

You always know what’s inside your network.

All of this makes agentless asset and vulnerability discovery a strong choice for modern IT security.

Challenges You Should Know

Nothing is perfect. Agentless asset and vulnerability discovery can face issues like:

limited access to deep system data

dependency on network permissions

difficulty with legacy systems

occasional blind spots

So companies often combine it with other tools for full coverage.

Real-World Use Cases

You see agentless asset and vulnerability discovery in:

Enterprise IT monitoring

Cloud security platforms

Compliance audits

Vulnerability management programs

SOC operations

Large organizations use it to keep track of thousands of endpoints without manual work.

The Future Of Agentless Discovery

The trend is clear. More AI, more automation, more integration. Reports show vendors now add AI-based anomaly detection and real-time vulnerability alerts to these tools to improve accuracy and speed. So, agentless asset and vulnerability discovery is not just growing. It is becoming smarter.

Conclusion

Modern networks change every second. Manual tracking cannot handle it. That’s why agentless asset and vulnerability discovery matters. It finds assets, detects risks, and keeps everything visible without heavy setup.

It works quietly in the background. But it gives security teams something powerful: control.

And in today’s digital world, control is everything.

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What is Operational Technology (OT) Security? And Why It Matters

In the current globalized society, industries have started to use digital systems to monitor physical operations. Operational Technology (OT), deals with millions of systems that provide fundamental processes, such as manufacturing facilities, power networks, and transportation systems among others. The more the systems are joined to the internet and the enterprise networks the more it is exposed to cyber attacks.

This is the point where OT security is important. It aims to secure hardware and software that supervise and manage industrial devices and infrastructure. Cyberattacks are capable of derailing activities, damaging equipment, and even threatening human lives in case of unprotected attacks. The significance of OT security enables businesses to protect their critical operations and remain reliable.

Understanding Operational Technology (OT)

Operational Technology is defined as controls over physical equipment, industry processes and infrastructure. Such systems are commonly applied in such industries as manufacturing, energy, transportation, water treatment, and healthcare.

Examples of OT systems include:

Industrial Control Systems (ICS)

Supervisory Control and Data Acquisition (SCADA) systems

Distributed Control Systems (DCS)

Programmable Logic Controllers (PLCs)

Smart sensors and industrial devices

OT systems are also unlike traditional IT systems, where data and communication are managed; machinery and production processes are also controlled by the OT system. Subsequently, due to this fact, any cyber attack on OT systems could have direct real-life impact.

What is OT Security?

OT security is defined as the practices, technologies, and strategies that prevent cyberattacks, unauthorised access, and operational disruptions to operational technology systems.

OT security primarily aims at guaranteeing safe and reliable operation of the industrial processes. It encompasses the protection of physical process controlling devices, networks and software.

OT security typically includes:

Monitoring industrial networks for suspicious activity

Preventing unauthorized access to control systems

Protecting industrial devices from malware and cyberattacks

Ensuring safe communication between machines and control centers

By implementing strong OT security, businesses can reduce the risk of downtime, accidents, and operational failures.

Why OT Security Matters

Protection of Critical Infrastructure

National critical infrastructure encompasses many industries that depend on operational technology. This consists of power plants, water treatment plants, transport, and manufacturing plants.

In case of a cyberattack on these systems, it may affect vital services. Good OT security assures safe and reliable operation of critical infrastructure.

Prevention of Industrial Cyberattacks

Industrial systems are also targeted by cybercriminals more often because they have potential consequences. Such attacks as ransomware, malware infection, and system manipulation may use it to close operations or damage equipment. A good OT security solution enables the identification of threats before they escalate and allows attackers to take control of critical systems.

Ensuring Worker and Public Safety

In contrast to the common IT attacks, which are mostly data-related, attacks on industrial systems have the potential to cause physical damage. A case in point, the weakened systems may lead to the malfunctioning of machines or the malfunctioning of safety systems. Strong OT security will help prevent dangerous incidents to the employees, nearby communities and the environment.

Reducing Operational Downtime

The work of industries is based on constant and reliable performance. Even a small inconvenience can cause a lot of financial losses.

Weak security can start production lines or disrupt services due to cyberattacks or system crashes. Good OT security reduces such threats and achieves operational continuity.

Compliance with Industry Regulations

Cybersecurity regulations and standards are very strict and must be adhered to in many industries. Cybersecurity in industries is becoming a concern for governments and regulatory authorities.

Subsequent OT security practices enable companies to comply with OT security standards and avoid legal fines or operational limitations.

Key Components of OT Security

To effectively protect operational systems, organizations implement several core security practices.

Network Segmentation

Separating OT networks from IT networks helps prevent cyber threats from spreading across systems. This creates an additional layer of protection for critical industrial processes.

Continuous Monitoring

Monitoring industrial networks helps identify suspicious behavior or unusual activity. Early detection allows organizations to respond quickly before damage occurs.

Access Control

Restricting who can access operational systems is an important security measure. Only authorized personnel should have access to control systems and sensitive infrastructure.

Patch Management

Keeping systems updated with security patches reduces vulnerabilities that cybercriminals could exploit.

Incident Response Planning

Having a clear response plan helps businesses react quickly if a security incident occurs. This minimizes damage and ensures faster recovery.

Challenges in OT Security

Although safeguarding operational technology is necessary, it also entails some peculiarities. A lot of industrial systems are years old, and they were not produced with cybersecurity in mind. Making changes to these systems can be complicated and expensive.

Moreover, OT systems usually require a constant level of operation, and it is hard to make updates or security modifications without disrupting operations. Another problem is the difficulty of integrating IT and OT systems more closely. On the one hand, this enhances efficiency, but it also increases the size of the attack surface that cyber threats can use.

The Future of OT Security

With the use of technologies that include IoT devices, smart factories, and Industry 4.0 solutions in industries, the operational technology systems are becoming more interconnected.

Such digital transformation is more efficient, yet it also introduces new cybersecurity risks. Consequently, the significance of OT security will increase. Companies are also investing more in state-of-the-art surveillance systems, AI-driven threat detection systems, and purpose-built industrial cybersecurity systems.

Conclusion

Operational technology is critical in managing systems that run industries and other critical infrastructure. As these systems continue to connect, though, they become susceptible to cyber threats as well.

The OT security should be implemented robustly to ensure the security of industrial processes, safety, and the continuity of operations, and to avoid cyberattacks. Through proper security measures, companies can protect their systems and make sure that the necessary services will remain running smoothly in the world of increased digitalization.

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Pentesting vs VMDR: What’s the Difference and Why You Need Both

Cyber threats are always dynamic and businesses can hardly afford to stick to one method of security. Attackers are always in search of vulnerabilities, misconfigurations and weak security controls. 

Organizations have been compelled to integrate various cybersecurity strategies in order to remain secure. Penetration testing (Pentesting) and Vulnerability Management, Detection, and Response ( VMDR ) are two methods that are used six.

Though they are concerned with enhancing security, they are used in varying circumstances. Knowing their differences and how they combine to provide a more effective defense strategy assists businesses in developing a more robust defense strategy. This article discusses the major distinctions and the reasons behind why pentest and VMDR platform will produce superior security results.

Understanding Penetration Testing (Pentesting)

Penetration testing is a simulated cyberattack that is performed by security professionals to determine exploitable weaknesses in systems, applications or networks. The aim is to reason like an attacker and identify how the vulnerabilities may be exploited to access unauthorized access.

Pentesting is more realistic in the case of actual attack situations as opposed to automated scanning. White hat hackers seek to bypass the safeguards, take advantage of security weaknesses and prove the potential business consequences.

Key Objectives of Pentesting

Identify exploitable vulnerabilities

Test existing security controls

Simulate attacker behavior

Evaluate response readiness

Provide remediation guidance

Pentesting is usually conducted periodically, such as annually or after major infrastructure changes.

Understanding VMDR (Vulnerability Management, Detection, and Response)

VMDR is an ongoing security program that recognizes, allocates, and assists in eliminating vulnerabilities within the setting of an organization. VMDR is run on a continuous basis to identify new risks as they arise instead of periodic testing.

A VMDR system will scan the systems, assess risk levels, and give continuous insight into areas of security risk. This will help in mitigating vulnerabilities before they are used.

Core Functions of VMDR

Continuous vulnerability scanning

Asset discovery and monitoring

Risk-based prioritization

Remediation guidance

Ongoing security visibility

A modern pentest and VMDR platform combines continuous monitoring with real-world validation to strengthen overall security.

Pentesting vs VMDR: Key Differences

While both approaches enhance cybersecurity, they differ in methodology and outcomes.

Approach

Pentesting is concerned with exploitation. Security experts will work hard to hack systems to show actual attack paths.

VMDR is concentrated on the identification and management, performing a constant scanning of vulnerabilities.

Frequency

Pentesting offers a snapshot analysis. VMDR is on-demand and real-time risk visibility.

Depth vs Coverage

Pentesting provides in-depth examination of the chosen systems or applications. VMDR is expansive in terms of all assets.

Output

Pentesting presents a detailed report of vulnerabilities that can be used. VMDR causes continuous vulnerability information and prioritization understanding. Because each addresses different security needs, many organizations adopt a pentest and VMDR platform to eliminate security gaps.

Why Pentesting Alone Is Not Enough

There is a myth that a pentest is undertaken once a year, and provides on-demand protection throughout the year. As a matter of fact, new vulnerabilities are highlighted on a regular basis as a result of updates, configuration or new threats that are discovered.

A pentest represents the current level of security at the moment of performing it. New risks can take months to be noticed without constant monitoring. VMDR meets this gap by establishing vulnerabilities between testing cycles.

Using a pentest and VMDR platform ensures vulnerabilities are continuously discovered and periodically validated through real attack simulations.

Why VMDR Alone Is Not Enough

VMDR offers constant visibility without necessarily displaying how vulnerabilities can be integrated in real attacks. Thousands of problems can be detected by automated scans, and it is hard to prioritize.

Pentesting assists in confirming that only high-risk and exploitable vulnerabilities are real. This helps security teams to spend their time on matters that may actually result in breaches and not all the vulnerabilities that have been detected. A combined pentest and VMDR platform helps reduce false positives and improves remediation efficiency.

How Pentesting and VMDR Work Together

Pentesting and VMDR are more effective security models when combined.

Continuous Discovery with Validation

The vulnerabilities are always identified by VMDR, and pentesting is used to determine the exploitability.

Better Prioritization

Pentesting assists in identifying which vulnerabilities are actually of business risk to enhance VMDR prioritization.

Improved Visibility

VMDR provides ongoing monitoring, and pentesting adds context through attacker-focused testing.

Faster Remediation

Combining both reduces the time between vulnerability discovery and resolution.

Organizations implementing a pentest and VMDR platform benefit from both proactive monitoring and realistic testing.

Business Benefits of Using Both

Stronger understanding of security risks

Improved compliance readiness

Reduced attack surface

Better use of security resources

By integrating both approaches, businesses align technical security efforts with real business risk.

Conclusion

Pentesting and VMDR are not defensive and offensive cybersecurity activities. Pentesting shows the way attackers are taking advantage of vulnerabilities whereas VMDR keeps the vulnerabilities identified and controlled. One gives depth and the other gives continuity.

Companies that do not adopt more than one strategy run the risk of having loophole insecurity. Pentest and VMDR platform is a combination that provides continuous assessment, realistic validation, and quicker remediation. The current threat environment is highly dynamic, and a dual strategy is crucial to achieving a robust security posture.

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Explaining Retrieval Augmented Generation: How RAG Increases LLM Accuracy and Trust

Retrieval Augmented Generation (RAG) is a modification to the way Large Language Models (LLMs) generate replies. Conventional LLMs only rely on fixed training data, limiting their accuracy and making them unable to access the current information. 

RAG expands this by linking models to external knowledge sources, grounding outputs in factual data. This not only boosts accuracy but also increases user confidence in AI systems. 

In fact, recent industry trends show that over 70% of enterprises deploy RAG to improve LLM relevance and reliability because traditional models often generate misleading outputs. RAG systems cut hallucinations by large margins by anchoring responses in trusted data. 

Let’s check how RAG works, illustrates its key benefits, and shows why it matters in today’s AI landscape:

What Is Retrieval Augmented Generation (RAG)?

Retrieval Augmented Generation (RAG) is a method that connects an LLM with external knowledge sources such as databases, documents, or proprietary datasets. A RAG -powered model does not solely rely on training data to come up with an answer, but it first retrieves the needed information and only then produces it. 

On posing a question, a user will have a semantic search that will help the system to establish the most relevant documents in the initial step. It then integrates the retrieved information with the prompt given by the user, to assist the model in giving a more accurate and context-sensitive response.

This combination of retrieval + generation gives RAG a major edge over standard LLMs.

How RAG Works: A Clear Workflow

User Query: A user sends a natural‑language question.

Embedding: The system converts the query into a numeric vector.

Retrieval: A vector database performs a semantic search to find matching documents or data segments.

Augmentation: The model receives the query plus the retrieved context.

Generation: The LLM produces a response grounded in both retrieved facts and language patterns.

This process helps RAG models reference real, relevant information every time they generate an answer. 

Why RAG Matters: Benefits for Accuracy and Trust

Improves Accuracy

Traditional LLMs often hallucinate by producing confident but incorrect info. RAG reduces inaccuracy by grounding answers in actual sources. Industry observations indicate RAG can reduce hallucination rates significantly compared to baseline models without retrieval support. 

By injecting new information at the point of generation, RAG gives LLMs access to the most current and relevant facts, improving output quality even when facing recent or domain specific queries. 

Keeps Information Up to Date Without Retraining

Model retraining for new knowledge is costly and slow. RAG sidesteps this by connecting the model to fresh data in real time. Teams can update the content repository without expensive retraining cycles. This makes RAG powerful for use cases where data changes quickly, such as regulatory compliance, technical support, and financial reporting.

Boosts Contextual Relevance

RAG improves contextual responses by retrieving source documents that align closely with the query. This means responses reflect domain‑specific knowledge instead of generalized patterns. This is crucial for industry applications such as legal reasoning, medical assistance, and technical documentation, where generic LLM answers often fall short.

Boosts User Trust Through Transparency

Because RAG can show where information originated, users get more than an answer—they get traceable context. This increases trust and accountability, especially in regulated industries where proof of data origin matters. Users can verify the sources a model used, helping them confirm facts or investigate further when needed.

Expands Use Cases Across Industries

RAG’s ability to combine generative capabilities with reliable information makes it valuable in many domains. Businesses use it for customer support, internal knowledge bases, research assistants, and personalized recommendations. Organizations that adopt RAG benefit from tailored AI solutions that perform consistently across use cases requiring precision and depth.

Supports Efficient Internal Knowledge Access

Employees spend significant time searching for internal information. RAG reduces this time by framing the right answers quickly from knowledge bases. It can improve productivity and help teams make faster and relevant decisions.

Challenges of RAG

RAG is powerful, but it has limits:

Data quality matters: If sources contain outdated or unreliable data, outputs may still mislead. 

Processing overhead: Retrieving and integrating information adds computational steps compared to plain LLM generation. 

Trust depends on retrieval criteria: If the retrieval mechanism pulls irrelevant context, accuracy may suffer despite the RAG architecture. 

These challenges reinforce the need for good data governance, clear indexing standards, and quality external sources.

RAG and Enterprise AI Adoption

Recent industry signals show RAG’s strategic role in enterprise AI. As businesses scale AI use, trust becomes a central concern. Almost half of developers cite accuracy concerns with generative AI tools, highlighting the need for solutions like RAG to build confidence in outputs. 

Conclusion: RAG Improves AI Reliability

Retrieval Augmented Generation changes how LLMs perform in real‑world settings. By combining external knowledge retrieval with powerful generative models, RAG:

Makes responses more accurate

Grounds content in real data

Reduces hallucinations

Enhances trust and transparency

Supports dynamic, domain‑specific applications

In an era where accuracy governs user trust and enterprise adoption, RAG stands out as a key advancement in generative AI technology. By enabling LLMs to reference up‑to‑date, context relevant information, RAG moves AI closer to reliable, actionable intelligence instead of guesswork.

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Reducing Development Risks Through SDLC Gap Analysis

Software development carries many risks. Teams face unclear requirements and design issues. Teams face testing gaps. These problems affect cost and quality. They also affect delivery timelines. So, organizations need a structured way to control these risks. Here, SDLC gap analysis offers that structure.

SDLC gap analysis helps teams compare current practices with expected SDLC standards. This comparison reveals weaknesses. It also reveals missing activities and helps teams gain clarity and take control of risks.

Understanding Development Risks in SDLC

58% of projects fail due to improper risk management and nearly 44% of projects fail due to poor planning. Development risks appear at every stage. These risks start during planning and grow during execution. They impact deployment and support. 

Common development risks include:

Incomplete requirements

Weak system design

Code inconsistency

Limited test coverage

Poor change control

Lack of documentation

Each risk links to an SDLC phase, and each phase needs discipline. SDLC gap analysis connects risks to their root causes. This connection helps teams focus on real issues.

What Is SDLC Gap Analysis

SDLC gap analysis compares expected SDLC processes with actual project practices. It highlights mismatches. It shows where teams skip steps. It shows where teams lose alignment.

This analysis reviews each SDLC phase. It examines inputs and outputs. It examines roles and responsibilities and makes the goal simple. 

SDLC gap analysis supports informed decisions. It guides process improvement and strengthens governance. It also improves accountability.

Why SDLC Gap Analysis Reduces Risk

Gartner research shows that over 70% of ERP (Enterprise Resource Planning) initiatives will fail to meet their real business goals by 2027, showcasing ongoing risks in large IT projects.

Risks grow when teams lack visibility and work in silos. SDLC gap analysis creates visibility across phases and promotes shared understanding.

Key risk reduction benefits include:

Early issue identification

Clear process ownership

Better requirement traceability

Better quality control

Enhanced stakeholder confidence

SDLC gap analysis helps teams act before risks escalate. It prevents rework and protects budgets.

Role of SDLC Gap Analysis 

Indeed, necessity is the mother of invention, but weak requirements cause failure. Many teams rush this phase, and rush creates gaps.

SDLC gap analysis reviews requirement practices. It checks documentation quality and stakeholder involvement. It also checks approval workflows.

This review ensures alignment with business goals and ensures clarity. 45% of project managers report that scope creep is their biggest challenge. But clear requirements reduce confusion and scope changes.

Addressing Design Risks Through SDLC Gap Analysis

Design translates requirements into structure. Poor design leads to fragile systems and increases defects. SDLC gap analysis evaluates design standards. It reviews architecture decisions and design reviews. It also checks validation methods.

This process improves consistency and scalability. Also, strong and intuitive designs reduce risks and supports maintenance.

Improving Development Quality With SDLC Gap Analysis

Code quality affects system stability, and inconsistent coding practices raise risk. Also, missing standards create defects. So, SDLC gap analysis examines development practices. It reviews coding standards and version control usage. 

This analysis strengthens discipline and promotes consistency. It supports collaboration, which further helps in lowering defect rates.

 Enhancing Testing Through SDLC Gap Analysis

Testing protects quality, but limited testing exposes systems to failure. Many teams underinvest in testing. SDLC gap analysis reviews test strategies. It checks test planning, defect tracking, and test case coverage. 

This review ensures alignment with requirements. It also ensures readiness for release. Strong testing reduces production issues.

Managing Deployment and Maintenance Risks

Deployment also has several operational risks. Poor planning causes downtime, whereas poor documentation slows support.

SDLC gap analysis reviews deployment readiness. It checks rollback plans, user training, and support processes. This approach keeps smoother transitions and long-term stability. 

Steps to do better SDLC Gap Analysis

Organizations need a clear approach. Each step builds on the previous step.

Key steps include:

Define SDLC standards

Document current practices

Compare expected and actual states

Analyse all gaps and risks

Implement process improvements

Each step supports clarity and control. Here, SDLC gap analysis succeeds through discipline.

Best Practices for Successful SDLC Gap Analysis

Teams must commit to improvement and take advice from leaders. Clear communication is also an essential part of the best SDLC gap analysis practices.

What are top recommended practices for successful SDLC gap analysis:

Hire cross-functional teams

Use measurable criteria

Maintain documentation

Review gaps regularly

Track improvement outcomes

These practices sustain value and support continuous improvement.

Long-Term Value of SDLC Gap Analysis

SDLC gap analysis supports maturity and strengthens governance. It also improves predictability. In this way, organizations gain consistent delivery, customer trust, and better quality. 

This approach reduces firefighting. It also supports strategic growth. Due to all these features, SDLC gap analysis has become a long-term asset.

Conclusion

Development risks challenge every project, and unchecked risks cause failure. Development teams need structure and insight to ensure a smooth SDLC process.

SDLC gap analysis provides both structure and insight. It aligns processes with standards and reveals hidden issues. Moreover, it helps in improving the software development process.

Organizations that adopt SDLC gap analysis reduce uncertainty. Hiring expert SDLC gap analysts helps you improve your software quality and deliver it with confidence in the market.

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Do you want to know about the tools used in a variety of VAPT (Vulnerability Assessment & Penetration Testing) services? We’ve compiled a list of these tools.

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Fight Threat and Reduce Risk with Managed security services from Kratikal. We help your businesses reduce cyber risk and combat cyber threat