Cybersecurity has emerged as one of the most pressing problems of businesses, governments, and individuals as the digital transformation increases in speed. Human error  is among the worst weaknesses and it follows that appropriate employee training is a key component of any strong security posture. Managed Detection and Response (MDR)  is the outsourced solution that provides the necessary expertise and 24/7 coverage to bridge the widening gap between attack speed and internal response capabilities. The blog contains the agenda on how to design and implement a successful cybersecurity training program. Key Components for your MDR Training Program Carry Out a Needs Assessment (MDR Focus: Define Scope and Data Sources) You need to be aware of the risks that are specific to your organization before creating a program. Analyze common threats, past incidents, and roles within the company. For MDR, this assessment means defining the scope of coverage  (endpoints, cloud, identity, network) and identifying all necessary data sources  (telemetry) for the provider. A successful deployment requires the MDR partner to analyze your environment's specific threat landscape and integrate with your existing security tools to establish baseline normalcy. It makes the training more relevant and productive as it allows adapting it to the real needs and weaknesses of your employees. Develop an Inclusive Curriculum (MDR Focus: Integrate Human Expertise & EDR) The curriculum should consist of the necessary topics that also cover how to detect phishing attacks, learn about the potential risks posed by RaaS, and how to behave safely on the internet. For MDR, the core curriculum is the combination of Endpoint Detection and Response (EDR)  technology with human threat hunting . This model enables proactive security  by allowing human analysts to actively search for subtle signs of compromise that automated alerts often miss (like "living off the land" attacks). It should also talk about the importance of good passwords and the security of cloud and hybrid environments since these misconfigurations can be a priority vulnerability in this scenario. Work With Interesting and Reachable Formats (MDR Focus: 24/7/365 Monitoring) Do not rely on long, heavy-text documents as the only way to ensure that the employees remain active; diversify the forms. MDR services achieve reachability by providing 24/7/365 coverage  via a Security Operations Center (SOC) staffed by expert analysts. This ensures that threats are detected and contained regardless of time zone or internal staffing availability . This continuous monitoring capability is the format through which MDR makes high-level, constant security accessible to organizations that cannot afford a dedicated in-house team. Making the training easily accessible and easy to understand will ensure that the training can be easily participated in and that the retention rate is addressed. Take a Gradual and Progressive Approach (MDR Focus: Containment and Remediation) There is no respite in cybersecurity threats which continue to evolve due to advances in artificial intelligence. Therefore, training cannot be a one day event. MDR takes a progressive approach by shifting the focus from mere alerting (like traditional MSSPs) to active response . When a genuine threat is identified, the MDR team's analysts immediately initiate remote containment  (e.g., isolating a compromised host) and provide guided or managed remediation  to neutralize the threat and reduce the attacker's dwell time  from months to minutes. Implement the program gradually and give employees regular, periodic refresher courses to ensure that they are updated on the latest threats like AI-based attacks and the rise of RaaS. Measure, Monitor, and Reinforce (MDR Focus: Reduced Risk and Reporting) To evaluate the performance of your training, you can measure it through performance like the clicks on phishing or security breaches. The value of MDR is measured through metrics like the dramatic reduction in Mean Time To Detect (MTTD)  and Mean Time To Respond (MTTR) . MDR providers continually reinforce security posture by offering detailed reports, root cause analysis , and actionable recommendations to fix vulnerabilities discovered during the response. Use these lessons to refine your curriculum and provide concrete and instant feedback. Gamification of these lessons or some form of rewards can also be used to reinforce positive security behaviors.

The popular mobile wallet platform GCash  has been officially cleared of wrongdoing following a rigorous investigation by the National Privacy Commission ( NPC ) into widespread claims of a massive customer data leak. The NPC concluded that after a technical audit, there was "no sufficient basis to conclude that a personal data breach occurred"  within the systems of G-Xchange, Inc., the company that operates GCash. The investigation was launched after reports and circulating dark web chatter in early October suggested that a massive trove of customer data, including Know Your Customer (eKYC) information, had been stolen from the platform. The circulating data set had generated significant public concern and speculation across social media, putting immense pressure on both GCash and the Philippine government's privacy regulator. To further solidify the findings, the NPC conducted a deep dive into GCash's operational security. This included a live technical demonstration  of the company's systems under the direct supervision of NPC investigators. The audit confirmed that there were zero incidents  of unauthorized access attempts on critical databases that house sensitive information. Furthermore, security experts performed an independent validation of the dataset being circulated online and determined that the structure and contents were inconsistent  with GCash's verified data architecture. GCash has actively worked to improve its safety protocols as a reaction to the growing controversy. Following the initial public reports of the alleged leak, the company worked closely with the NPC, the Cybercrime Investigation and Coordinating Center (CICC), and the Bangko Sentral ng Pilipinas (BSP) to promptly address the claims. The e-wallet provider's swift, cooperative action and the demonstrable integrity of its current security infrastructure proved crucial in the investigation's final outcome. In spite of these attempts, critics claim that these actions are not enough and the company took care of its growth and profit long before the security of the most vulnerable users. While the NPC's conclusion absolves the company of a direct system breach, the public outcry and the intense regulatory scrutiny highlight the ongoing controversy over whether a major financial technology corporation has the proper role in defending customer trust in the digital age. The results of such official government inquiries are supposed to provide historic reassurance to millions of users concerning how major fintech platforms will be accountable for keeping data safe in the future.

In Southeast Asia, the geopolitical attention is no longer paid to maritime chokepoints, but to digital fault lines, with the new Prime Minister of Japan, Sanae Takaichi, using her first diplomatic speech to the ASEAN Summit to declare a landmark program to strengthen the cybersecurity and artificial intelligence (AI) operations of the bloc. The commitment that Takiichi referred to as an attempt to beef up cooperation primarily aims at the increasing threats to the region, such as complex state-sponsored espionage and syndicates of organized cybercrime that are now using high-tech AI weaponry. This action is also in line with the vision of the Free and Open Indo-Pacific (FOIP) by Japan, and the commitment is to ensure that a rules-based order is upheld against disruptive actors in the physical and cyber space. Extreme urgency of this alliance is explained by the recent reports according to which AI-enhanced threats increase dramatically. Cyber-attackers are using Large Language Models (LLMs) to automate and scale attacks, both to engineer multilingual phishing control to infiltrate an organization and offload the overhead of financial fraud to organized crime cells. As the countries of the region are quickly digitizing, the Philippines is not an exception, a recent study revealed that the number of people who have adopted AI to enhance security has spiked in a short time, which means that the mutual susceptibility to a system-wide breach has never been higher. The cooperation framework will focus on three main aspects: creating a Japan-ASEAN AI Partnership around the Human Resources training and capacity building to bridge the talent gap; collaborating to construct a safe, secure and reliable AI ecosystem, understanding the fact that the high-speed adoption of AI should be secured by the same regulatory standards in order to avoid abuse of AI; and aligning enhanced cybersecurity activities with maritime security issues, realizing that the integrity of data and communication networks cannot be separated with the stability of the region. The focus of Takiichi on a coordinated defense strategy is important to note that no single country in Southeast Asia will be in a position to protect itself against such transnational, AI-driven threats. Japan is not only exerting influence by assuming a regional, collective defense stance, but also providing a deterrent layer against those that seek to destabilize the region by employing digital means. Nevertheless, the project also helps to remind that another side of the coin of emerging technology, the same AI tools that drive economic growth are also the ones that enable the creation of new security threats never seen before. The effectiveness of this coalition will rest on its capacity to go beyond mere technology transfer to develop a mutual level of operational convergence that has the capacity to react to the more automated nature of cyber war. The result should become the historic precedent of how the global powers will cooperate in guaranteeing the digital sovereignty and safety in the world that is highly interconnected.

The classical model of cybersecurity, which is the creation of high impassable barriers to identify and respond to any potential threat identified by the security agencies, is not working with fluid and sophisticated attackers. To launch and operate long term campaigns, attackers tend to utilize a network with predictable layouts, target architecture and deterministic network configurations. The answer lies in the paradigm shift in strategy of reactive detection to proactive protection by Entropy Injection. Entropy in this case is unpredictable or random. By introducing chaos into the environment, which is always brought in and deliberately introduced, organizations can make the environment they operate in as unpredictable as possible and in so doing manage to make the information they have reconnoitered in the past become outdated the moment they receive it. The critical points of the creation and deployment of an effective, entropy-based defense program are stated in the following post. Important Elements of your Entropy Injection Program. Identify Attack Surfaces and Predictability of Baseline System. First of all, you should single out clearly the natural vulnerabilities of your setting due to predictability prior to installing any form of defense. Carry out a complete audit to list all the fixes: memory allocation policies, fixed IP addresses, standard application paths, default user port etc. Order systems in terms of how susceptible they are to targeted attacks in which predictability is advantageous (e.g., a server with a legacy code that is important to its functioning). What you are doing here is determining the quantity of current level of the baseline entropy of all the major components and where an attacker is most likely certain and, therefore, has the minimal trouble to get it. Reimburse the Layered Randomization Techniques. The methods of randomization must all be done in a synchronized mode so as to come up with a great defense in your proactive defense curriculum. At the host level, high-entropy Address Space Layout Randomization (ASLR) and data perturbation can be used to reduce memory corruption exploits. The approach of dynamic IP assignment and port hopping the critical infrastructure must be deployed at the network layer in such a way that reconnaissance by the enemy is a waste. The idea behind such a multi-layered technique is to obscure the critical data points using entropy so as to confuse the network topology and force attackers to guess-attack, and consequently, bring them to an almost complete halt. Pay attention to Shifting and Short-lived Defense Measures. Do not rely on the same security measure; and ensure that you have those measures in place that will differentiate the defensive position at any one time. These include complete adoption of the Moving Target Defense (MTD) strategies. These systems automatically generate mutable attack surfaces, e.g. by reconfiguring system settings periodically, by reconfiguring virtual machine snapshots or reconfiguring network paths. This automotive instability and strategy does not allow any data acquired by the attacker at the specified point of time to be used in the next and this makes the preparation of an attack prohibitively costly and time-consuming. Cyber Kill Chain Leap into Entropy. Randomness should be introduced at a slow pace and in stages where principle of uncertainty should be applied at all stages of the Cyber Kill Chain. During reconnoitering, there should be deception and noise injection to confuse attackers with useless targets and misleading information. At the delivery and exploit stages, high-randomization of memory with high entropy is performed to destroy the code of exploit. Lastly, after an attacker has established a foothold, process randomization by continuous processes as well as process control flow integrity checking with the means that the attacker can never be certain of his persistence and privilege attack. Determining the Uncertainty and tracking Attack Signatures. In order to clarify the fact that this new defense paradigm is helpful, you have to determine the level of performance. The latter will necessitate the rejection of percentages of detection in calculation of the Uncertainty Score- the amount of the complexity and randomness of your world, in comparison to known adversarial models. Record the traces of unsuccessful deeds to attack (e.g. several unsuccessful attempts of brute force at randomized addresses) in the monitor system of the evidence of attacks aimed at compromising the evidence. Monitor these measures to keep on maximizing your parameters of randomization and demonstrate that you are already uncertain with regard to your active defense policy and costing the opponent more to conduct business.

The period of a free development of the artificial intelligences is expanding quickly. Following decades of guidelines and soft standards, there is a surge of binding legislation that is sweeping the world. One of the leaders in this respect is the AI Act of the European Union, a pioneering regulation that is leading in making it mandatory across the globe. This change implies that the reactive approach to businesses that utilize AI in various forms for generative tools or automated decision-making systems is not an option anymore. The necessity to implement a full-scale strategy of compliance, which is proactive, has become an essential business priority. The following should be a blog post that will describe the state of the regulatory environment and elaborate how the key framework to prepare your organization is to be built in order to be ready by the next age of AI compliance. Important Elements of your Strong AI Compliance Program. Identify Your AI Risk Profile. The first thing that you need to be aware of is the regulatory and ethical risks unique to the use of AI in your organization. Have a look at all your current and intended AI systems and categorize them based on a risk model, including the four categories called in the EU AI Act ( Unacceptable, High, Limited, Minimal ). When systems are considered to be High-Risk (e.g. those utilized in hiring, lending, or critical infrastructure), then you need to be very strict and outline the possible harms, bias, and vulnerabilities. Such an evaluation is the sole means of adjusting your compliance undertakings to the actual liabilities in your systems. Develop an Effective AI Governance Framework. Your AI Governance Framework is your AI compliance curriculum. Such a framework should not just be composed of technical teams, but also of senior representatives of Legal, Compliance, Data Science, and Business Unit. The framework needs to adopt fundamental principles, such as those in the NIST AI Risk Management Framework (AI RMF) which include Govern, Map, Measure, and Manage. It should establish clear roles, responsibility, and ethical principles, as well as making sure that the elements of fairness and transparency are incorporated in the very first development process of any AI product. Make Sure of Auditable Records of Compliance. Compliance does not only require compliance, but demonstration of compliance. Do not trust unrecorded, ad hoc processes, but rather enforce documented, standardized processes. This involves developing AI Inventories (a list of all models in operation), Model Cards or AI Factsheets (documenting the purpose of a model, its data and restrictions), and other logs of all automated decisions. It is important to keep this documentation readily available and updated so as to have successful regulatory audits. Embrace an Integrated, Flexible Compliance Policy. Considering that the international environment is intricate (those compulsory EU rules versus the US requirements), your compliance initiative cannot be a local once-only affair. Use the global mindset to implement the program, where a standard that is above the top (usually the EU Act) is used and tailor it to the requirements locally. Offer training and updates to employees and developers on a regular, periodical basis regarding changing laws and new technical demands, including, but not limited to Explainable AI (XAI) methods or new rules regarding the labeling of Generative AI. Check the Performance and Trace the Accountability. Continuous monitoring needs to be embraced by you so as to justify the performance of your compliance program. The adoption of mechanisms that continuously monitor model drift (performance degradation), data bias and adherence to ethical policies in real-time. Utilize these measurements to perfect your models as well as your governance policies. In the case of high-risk decisions, introduce a Human-in-the-Loop (HITL) process, which implies that qualified staff have to address and verify important outputs, which will ensure human responsibility and control of the AI system.

The fast adoption of Large Language Models (LLMs) such as ChatGPT by bad actors is intensifying cyber threats all over the world, and recent reports indicate that the spies of countries and organized criminal gangs are integrating AI as a global threat to dramatically amplify the size and effectiveness of their existing activities. This trend is defining a global threat environment, which is more automated and sophisticated. This shift is recorded in the recent intelligence reports of major technology companies, such as OpenAI and Microsoft, which outline the increasing usage of generative AI to mechanize the most important processes in cyber campaigns. Actions that are observed demonstrate actors do not develop some fresh attacks, they are just gluing AI tools to the existing malicious playbooks. The complaints regarding the technology are: Malware Refinement: Russian-language cyber crews have been actively attempting to apply models to hone elements of high-end malware, including credential stealer and remote-access trojan malware. Espionage Automation: The Chinese government-affiliated groups are training AI to create more of these phishing messages and context-aware, and debug complicated code, making their intelligence-collection tasks easier. C2 Development: operators using Korean language have been discovered making use of the LLMs to create advanced command-and-control (C2) systems to their networks. To make the menace even more intense, advanced organized crime syndicates operating in other regions such as the Southeast Asian are automating huge financial frauds using AI. These organizations apply the models to decode poorly written scam messages into fluent and persuasive English and to develop believable artificial media and elaborate fake investor personalities of falsely alleged investment ventures. The technical barrier is also reduced due to the simplicity of creating custom, high-quality content, and Microsoft states that now more than a half of the attacks with known motives are extortion and ransomware-motivated. Roblox has responded to the rising controversy by implementing better safety measures. At the beginning of September, the company introduced a new age estimation feature, a machine-learning-based tool that can group users based on age. This feature, combined with the implementation of the new direct messaging controls represents the element of the work of Roblox to address the safety issue and transform the platform into a more safe environment in which its younger audience could enjoy it. On the one hand, AI is utilized to boost the speed and scope of attackers, but the technology is also becoming an important instrument to protect the world. In a recent report, OpenAI reported that its models were utilized by regular users more often on three occasions than they were by malicious actors to create scams. Despite such efforts, critics argue that such measures of Roblox are insufficient and the company had taken into account its expansion and profitability long before the safety of the weakest users. The legal action and media furor are some of the existing debates regarding whether a technology company can advocate children online or not. An outcome of such suing cases is expected to give historic precedents of how online platforms will be held accountable to ensure children safety in future.

Geopolitical wars in the world are no longer being fought on the usual battlefields. The new front lines are now into the digital world, making cyberspace a new battlefield of the state-sponsored and state--aligned. What used to be regarded as the territory of nation-states has become a burning issue of concern in all businesses irrespective of size and sector. Cyberwarfare is no t only government spying, but introduces a high probability of the so-called spillover effects that may cause damage to important infrastructure, cripple economies and even affect nongovernmental organizations that have nothing to do with the combat between two states. It is a guide that will outline the core actions that your organization can take to prepare against these increasing threats and develop the necessary resilience to cyberwarfare. A Corporate Resilience Framework to Cyberwarfare. Knowing the New Digital Frontline of Cyberwarfare. You have to come to the first place to acknowledge that your organization is a possible target or collateral damage of geopolitical conflict. This entails going beyond the simple risk measurement and incorporating the potential of a hybrid warfare, where the physical and cyber attacks are intertwined, and the likelihood of spillover effects of the attacks by country states (such as the example of NotPetya). Assess your assets and relationships to both critical sectors (finance, energy, government) and geopolitical hot zones as a way of comprehensively understanding your threat model of cyberwarfare. Bring Cybersecurity to the Boardroom. Cybersecurity is not an IT issue anymore but one of the basic business risks. Your board and executive leadership should be at work. This drill involves scheduling and carrying out tabletop exercises that mimic the situation of cyberwarfare to test decision-making under a time of great need. Besides, get proper disconnection plans- strategies that outline how easily and safely your most important systems may be isolated or shut down in case of an imminent threat being detected. Strengthen Your Back-office Security. Preparation begins with simple things, when it comes to the basic, though carried out with extreme discipline. Implement Multi-Factor Authentication (MFA) on all accounts and services to fight credential theft which is the main gate used by state actors. Intense patch management to address the identified vulnerabilities. Otherwise, against the attacks of destructive wiper malware, which is a fundamental tool of cyberwarfare, be sure to isolate your backup and recovery systems and make them immutable and tested regularly to ensure quick recovery. Take on a Zero Trust and High-Vigilance Posture. You should assume breach during a period when there is an escalation of tension in the world. Embrace a Zero Trust architecture that will take the principle of never trust, always verify to all users and devices that will access your network. More importantly, your security teams need to reduce the intrusion detection thresholds meaning that they should treat what would otherwise be viewed as a false positive as a real menace. This dynamic strategy is largely based on real-time threat intelligence on existing geopolitical targets and cyberwarfare strategies. Ensure Security of Supply Chain Ecosystem. In as much as the new bill in the UK focused on the supply chain, you need to understand that your partners can be your weakest link. Attackers sponsored by the state often attack smaller, poorly-guarded suppliers to have a bigger target. You need to carry out more thorough vendor risk analyses of any supplier of your critical systems, particularly one which supplies in or around conflict zones. The process of collaboration and sharing of threat intelligence with your partners and other relevant government agencies are now a part and parcel of your defense mechanism to cyberwarfare.

This can be described as a controversial development of the Department of the Interior and Local Government (DILG) as it examines a reported major data breach that has the potential of converting up to 40 million records of the Full Disclosure Policy Portal (FDPP). The event, which was disclosed by the cybersecurity advocacy group Deep Web Konek, was a pivotal point in terms of cybersecurity of the Philippine government, as the attacks on the digital resources of states began to grow. The investigation of DILG follows the reports that the alleged attacker was capable of leaking around 22 gigabytes of sensitive information. It is a complainable fact that this cache allegedly contains important audit trails, system logs, as well as user information, and this makes one question the integrity as well as the security of government information systems. To make matters worse, the supposed offender also marred a DILG web directory, and left a message that openly lamented about corruption in government projects - an idea that is similar to the recent public frustration. This is a suspected attack that is in an increasing wave of cyber attacks on the government agencies in the Philippines. A series of planned attacks earlier in the month of September defaced a variety of other government websites including those belonging to the Department of Public Works and Highways and the Department of Foreign Affairs. Although the Department of Information and Communications Technology (DICT) was quick to restore those sites online, claiming that there was no sensitive information that was lost during the process, the number of efforts is staggering. The government websites have already been targeted more than 1.4 million times by hackers according to officials of DICT, which demonstrates that the country keeps being under constant pressure of digital infrastructure. The time of this so-called DILG violation is especially crucial. It adds to the persisting debates among legislators, who have been putting the DICT on its toes to solve the issues of cybersecurity in the country urgently. There have been constant reservations on the high dependency of the country on foreign cloud services where up to 90 percent of the government data is presently held in foreign countries. This dependency does not only present serious threats to national security, but also cost taxpayers a lot in terms of money every year. Cybersecurity experts are strengthening their demand to take immediate, holistic action in response to the so-called DILG breach, and the overall environment of threats. This encompasses the prompt fixes, extensive forensic examination of the hacked systems, and a long-term change to strengthening local information infrastructure and increasing proactive defense systems. The investigation of these 40 million records that the DILG will conduct will likely establish precedent that is vital to how the agencies of the Philippine government handle and safeguard data on their citizens against an even more advanced cyber attackers going forward.

The business world is facing increasing cybersecurity scrutiny after a high-profile, advanced deepfake scam cost a multinational engineering firm Arup about $25 million (HK$200 million). It is an eye opener because, as Hong Kong police confirmed, the incident did not involve breaking into systems, but rather using advanced Artificial Intelligence to masquerade as a company leader and to play upon the trust of an employee. The latest and perhaps the most alarming development in this new era of cybercrime was a scenario in which finance staff with Arup in its Hong Kong office was duped into approving a huge transfer of funds. The scam started with an email, but the first inkling of arousal was, altogether, surpassed by the employee once he was invited to a supposed video conference. The employee was called by what seemed to be the Chief Financial Officer (CFO) of the company based in the UK and a few other employees. The case is one of a flood of deepfake-based frauds rocking the world, with specialized AI applications being used to manufacture authentic video and voice impersonations of executives. The crux of the complaint in these new offenses is that the technology has become so available and persuasive that it circumvents conventional human and technical scrutiny controls. The Arup employee acting under the assumption that he or she was carrying out urgent, confidential instructions issued by trusted leadership executed 15 individual wire transfers. All of the faces and voices on the call were identified afterward as an AI-created deepfake. To make the corporate pressure even more annoying, cybersecurity professionals and law-enforcement agencies are proclaiming that it is the new phase of technology-enhanced social engineering. Criminals are now being accused of intentionally using generative AI tools, which take little technical expertise to operate, to launch highly targeted and deceptive attacks. Rob Greig, Global Chief Information Officer of Arup has publicly admitted that the internal systems of the company were not compromised, which further confirms that the human factor is currently the weakest link in the defense chain. Corporations and security firms are dashing to enhance their inside security measures in response to the increasing controversy. Experts are now encouraging the use of non-technological measures, like creating a private, pre-arranged code word or safe phrase to use on senior-level financial approval and a mandatory, multi-channel verification rule so that a video request has to be verified by a separate, trusted phone call or face-to-face verification. Despite such efforts, opponents argue that these efforts are merely reactive and that tech regulators have been distressingly slow in developing legal frameworks around synthetic media. The judicial procedures and the media furor surrounding the Arup heist are instances of the continuing debate about whether AI firms should be tasked with protecting corporate employees and the financial system against malicious usage of their technology. The outputs of such inquiries are expected to present historic precedents on how online platforms and developers will answer to the when and how of the future fraudulent implementation of their AI models.

One remembers the times of cybersecurity when a firewall was simply a gatekeeper, which blocked what was bad and allowed what was good using fixed rules? Ok the digital world has evolved and so do the threats. The modern cyberattacks are shape-shifters and are continuously changing to bypass conventional protection mechanisms. This is where adaptive cybersecurity is involved, and dynamically retrainable firewalls are at the center of this. These are not the firewalls of your grandfather, but rather smart systems that learn, adapt and anticipate threats as they happen. What makes these next-generation defenses so radical and why they are becoming indispensable to the current protection will be explored in this blog post The History of dynamically retrainable firewalls. Further than Deterministic Rules: Why Retrainable Firewalls are Necessary. Conventional firewalls work on a predefined rules list. Provided that the traffic fits a rule that is known to be bad, it is blocked; correspondingly, it is permitted to pass over a rule known to be good. This is good with familiar threats, but it does not do well with unfamiliar, unknown or very dynamic attacks. The nature of modern threats, which are usually driven by the AI itself, can readily change their approach, and thus quite new defenses become outdated almost immediately after they are implemented. The new game with dynamically retrainable firewalls is that it does not rely on these set of rules but, with each arrival of new data, continuously updates its protective policies and responds to them without human intervention. Machine Learning: Driving the Adaptive Firewall Brains. So, how do firewalls get "smart"? Machine Learning (ML) is the answer. Rather than human administrators penning out each and every rule, ML-algorithms are informed by incredibly large volumes of network traffic information- both legitimate and malicious. The firewall then applies this information to determine patterns, and detect anomalies and even anticipate threats. Upon the appearance of a new threat or the variation of network conditions, the ML model can automatically retrain itself with the adaptation of its policies and rules on-the-fly. This enables the firewall to automatically make itself as effective as possible, thus a proactive instead of a reactive security device. On-the-fly Adaption and Policy Optimization using Dynamic Firewalls. What about a security system that does not simply block a known virus, but actually learns the nature of new and never-before-seen malware and develops a new defense rule dynamically. Dynamic retraining is that powerful. This is not limited to malware but a dynamic firewall can adjust to a changing user behavior, new applications, or change in network topology (adding new cloud services). Its continuous optimization of its policies guarantees that its security measures will be always up to date, and efficient against the most recent threats, which will lower the time interval of vulnerability that most attackers can take advantage of. The importance of dynamically retrainable firewalls to your organization. There is a strong benefit of adopting dynamically retrainable firewalls. They drastically decrease the amount of manual labor needed to handle security policy management, and allow IT employees to work on more important projects. More to the point, they offer an enhanced security against advanced attacks, such as zero-day exploits and polymorphic malware that can easily evade the conventional security systems. With the growing sophistication of cyber threat represented by the use of intelligent and automated threat systems, it is not only that having an equally intelligent and adaptive defense system is not only an advantage, but it is rapidly becoming a prerequisite to effective organizational security.