Organisations today face the need to balance strategic goals and operational responsibilities with growing security concerns.
For many companies, the exponential growth of heterogeneous operational environments has introduced new security challenges.
Organisations that depend on security controls associated with traditional monitoring solutions often voice concerns, which include a lack of visibility into their technology assets.
These assets can include growing numbers of managed or unmanaged internet of things, operational technology and IT operations management devices.
Without a full inventory of these assets, organisations often struggle to prioritise security efforts, leaving potential vulnerabilities unaddressed.
To compound matters, modern hybrid and multi-cloud infrastructures create complexities that challenge organisations’ ability to maintain comprehensive network control and achieve optimal capacity planning.
With sophisticated cyber attacks on the rise, a modern, comprehensive security framework that identifies devices, assesses their risks and automates appropriate responses to security challenges, is required.
The key to success in this regard is ever-evolving integration technology.
Professor George Westerman, of the MIT Sloan School of Management, says integration technology plays a critical role inbreaking down internal barriers and fostering interconnected processes across digital and organisational landscapes.
Integration technology represents the backbone of a proactive security system.
More pointedly, Amit Yoran, chairman of a leading global security consultancy, stresses that integration technology is crucial in aggregating and analysing network data, enhancing visibility to detect and respond to threats in real-time.
Both Westerman and Yoran allude to the importance of layered, cohesive approaches in security, where integration technology enables comprehensive visibility and more agile defences, moving beyond single-point solutions to a more interconnected and responsive security strategy.
Integration technology has its roots in the technologies of the 1980s and 1990s, including electronic data interchange, enterprise application integration and service-oriented architecture.
And it has accompanied the rapid adoption of cloud-based applications and software-as-a-service and integration-platform-as-a-service solutions that characterise today’s diverse and distributed environments.
New, forward-looking iterations of integration technology boosted by artificial intelligence engines, machine learning and behavioural analytics models have arrived on platforms that feature such advances as real-time data streaming, event-driven architectures and low-latency requirements.
These platforms are able to enhance security by many orders of magnitude by being able to mine and leverage the corporate network's rich device metadata repositories.
This sets the stage for the development of enhanced security systems that include advanced device identification, user and access intelligence, comprehensive network details, as well as security and compliance information together with behavioural data.
In addition, modern cloud-based platforms are able to aggregate device metadata from various sources, including unique identifiers such as IP and MAC addresses, as well as device manufacturers and hundreds of vertical branded cyber security stacks.
As a result, they are geared to proactively mitigate increasingly sophisticated cyber asset risks, while remediating vulnerabilities, blocking threats and protecting entire attack surfaces.
The platforms are capable of evaluating every possible device connected to the network, including endpoint detection and response systems, intrusion detection/prevention systems and network access control systems.
They also scrutinise device activity, study vulnerability data and granularly investigate behaviour patterns to produce comprehensive risk scores.
The data gathered can be used by network security enforcement points to good effect. Defined policies – often predefined by the end-user − could then trigger automated actions based on risk scores and behavioural insights.
For example, if a device has a high-risk score, the policy might automatically generate alerts to inform network administrators, feeding information into security information and event management systems; security orchestration, automation and response platforms; network access control solutions; and other cyber security tools, allowing the network to adapt its response to detected threats.
It could also restrict device access to critical resources or sensitive data or quarantine the device by moving it into a separate, isolated network segment, where its actions are limited and closely monitored.
Moreover, if a device exhibits highly-suspicious or malicious behaviour, the system can automatically disconnect it from the network to prevent further risk.
Against this backdrop, the need for real-time decision-making and enforcement is essential. By employing AI to help automate decisions at all security enforcement points, the network can minimise the time and resources needed to manage threats, ultimately improving operational efficiency.
Importantly, data on device actions, following a policy enforcement, can be sent back to AI-equipped risk assessment engines to refine future response strategies. This creates a continuous learning and improvement loop.
By automatically identifying, assessing and responding to device risks, the network can quickly contain threats and prevent the lateral movement of malicious actors.
Policy-based actions − which can be based on asset criticality in terms of the functions and efficiency of an organisation − help to create an ecosystem of trust that ensures devices adhere to compliance standards.
Integration technology represents the backbone of a proactive security system, turning metadata into actionable insights that reduce the attack surface and enhance an organisation’s overall defence posture.
Integration technology will continue to evolve as organisations seek greater agility, resilience and automation. Its future will likely see significantly deeper applications of AI, edge computing and decentralised architectures for seamless connectivity across all environments.
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