Search This Blog
Powered by Blogger.

Blog Archive

Labels

Footer About

Footer About

Labels

Load More
Trendy Right Now

Popular Posts

Showing posts with label AI Risk Management. Show all posts
Threat Intelligence
agentic AI AI Risk Management Cloud Governance Cyber defense Cyber Security Data Resilience Identity Protection SaaS security security automation Threat Intelligence

Balancing Rapid Innovation and Risk in the New Era of SaaS Security


 

The accelerating pace of technological innovation is leaving a growing number of organizations unwittingly exposing their organization to serious security risks as they expand their reliance on SaaS platforms and experiment with emerging agent-based AI algorithms in an effort to thrive in the age of digital disruption. Businesses are increasingly embracing cloud-based services to deliver enterprise software to their employees at breakneck speed. 

With this shift toward cloud-delivered services, it has become necessary for them to adopt new features at breakneck speed-often without pausing to implement, or even evaluate, the basic safeguards necessary to protect sensitive corporate information. There has been an unchecked acceleration of the pace of adoption of SaaS, creating a widening security gap that has renewed the urgent need for action from the Information Security community to those who are responsible for managing SaaS ecosystems. 

Despite the fact that frameworks such as the NIST Cybersecurity Framework (CSF) have served as a guide for InfoSec professionals for many years, many SaaS teams are only now beginning to use its rigorously defined functions—Govern, Identify, Protect, Detect, Respond, and Recover—particularly considering that NIST 2.0 emphasizes identity as the cornerstone of cyber defenses in a manner unparalleled to previous versions. 

Silverfort's identity-security approach is one of many new approaches emerging to help organizations meet these ever-evolving standards against this backdrop, allowing them to extend MFA to vulnerable systems, monitor lateral movements in real-time, and enforce adaptive controls more accurately. All of these developments are indicative of a critical moment for enterprises in which they need to balance relentless innovation with uncompromising security in a SaaS-driven, AI-driven world that is increasingly moving towards a SaaS-first model. 

The enterprise SaaS architecture is evolving into expansive, distributed ecosystems built on a multitenant infrastructure, microservices, and an ever-expanding web of open APIs, keeping up with the sheer scale and fluidity of modern operations is becoming increasingly difficult for traditional security models. 

The increasing complexity within an organization has led to enterprises focusing more on intelligent and autonomous security measures, making use of behavioral analytics, anomaly detection, and artificial intelligence-driven monitoring to identify threats much in advance of them becoming active. 

As opposed to conventional signature-based tools, advanced systems can detect subtle deviations from user behavior in real-time, neutralize risks that would otherwise remain undetected, and map user behavior in a way that will never be seen in the future. Innovators in the SaaS security space, such as HashRoot, are leading the way by integrating AI into the core of SaaS security workflows. 

A combination of predictive analytics and intelligent misconfiguration detection in HashRoot's AI Transformation Services can be used to improve aging infrastructures, enhance security postures, and construct proactive defense mechanisms that can keep up with the evolving threat landscape of 2025 and the unpredictable threats ahead of us. 

During the past two years, there has been a rapid growth in the adoption of artificial intelligence within enterprise software, which has drastically transformed the SaaS landscape at a rapid pace. According to new research, 99.7 percent of businesses rely on applications with AI capabilities built into them, which demonstrates how the technology is proven to boost efficiency and speed up decision-making for businesses. 

There is a growing awareness that the use of AI-enhanced SaaS tools is becoming increasingly common in the workplace, and that these systems have become increasingly integrated in every aspect of the work process. However, as organizations begin to grapple with the sweeping integration of AI into their businesses, a whole new set of risks emerge. 

As one of the most pressing concerns arises, a loss of control of sensitive information and intellectual property is a significant concern, raising complex concerns about confidentiality and governance, as well as long-term competitive exposure, as AI models often consume sensitive data and intellectual property. 

Meanwhile, the threat landscape is shifting as malicious actors are deploying sophisticated impersonator applications to mimic legitimate SaaS platforms in an attempt to trick users into granting them access to confidential corporate data through impersonation applications. It is even more challenging because AI-related vulnerabilities are traditionally identified and responded to manually—an approach which requires significant resources as well as slowing down the speed at which fast-evolving threats can be countered. 

Due to the growing reliance on cloud-based AI-driven software as a service, there has never been a greater need for automated, intelligent security mechanisms. It is also becoming increasingly apparent to CISOs and IT teams that disciplined SaaS configuration management is a critical priority. This is in line with CSF's Protect function under Platform Security, which has a strong alignment with the CSF's Protect function. In the recent past, organizations were forced to realize that they cannot rely solely on cloud vendors for secure operation. 

A significant share of cloud-related incidents can be traced back to preventable misconfigurations. Modern risk governance has become increasingly reliant on establishing clear configuration baselines and ensuring visibility across multiple platforms. While centralized tools can simplify oversight, there are no single solutions that can cover the full spectrum of configuration challenges. As a result of the recent development of multi-SaaS management systems, native platform controls and the judgment of skilled security professionals working within the defense-in-depth model, effective protection has become increasingly important. 

It is important to recognize that SaaS security is never static, so continuous monitoring is indispensable to protect against persistent threats such as authorized changes, accidental modifications, and gradual drifts from baseline security. It is becoming increasingly apparent that Agentic AI is playing a transformative role here. 

By detecting configuration drift at scale, correcting excessive permissions, and maintaining secure settings at a pace that humans alone can never match, it has begun to play a transformative role. In spite of this, configuration and identity controls are not all that it takes to secure an organization. Many organizations continue to rely on what is referred to as an “M&M security model” – a hardened outer shell with a soft, vulnerable center.

Once a valid user credential or API key is compromised, an attacker may be able to pass through perimeter defenses and access sensitive data without getting into the system. A strong SaaS data governance model based on the principles of identifying, protecting, and recovering critical information, including SaaS data governance, is essential to overcoming these challenges. This effort relies on accurate classification of data, which ensures that high-value assets are protected from unauthorised access, field level encryption, and adequate protection when they are copied into environments that are of lower security. 

There is now a critical role that automated data masking plays in preventing production data from being leaked into these environments, where security controls are often weak and third parties often have access to the data. In order to ensure compliance with evolving privacy regulations when personal information is used in testing, the same level of oversight is required as it is with production data. This evaluation must also be repeated periodically as policies and administrative practices change in the future. 

Within SaaS ecosystems, it is equally important to ensure that data is maintained in a manner that is both accurate and available. Although the NIST CSF emphasizes the need to implement a backup strategy that preserves data, allows precise recovery, and maintains uninterrupted operation, the service provider is responsible for maintaining the reliability of the underlying infrastructure. 

Modern SaaS environments require the ability to recover only the affected data without causing a lot of disruption, as opposed to traditional enterprise IT, which often relies on broad rollbacks to previous system states. It is crucial to maintain continuity in an enterprise-like environment by using granular resilience, especially because in order for agentic AI systems to function effectively and securely, they must have accurate, up-to-date information. 

Together, these measures demonstrate that safeguarding SaaS environments has evolved into a challenging multidimensional task - one that requires continuous coordination between technology teams, information security leaders, and risk committees in order to ensure that innovation can take place in a secure and scalable manner. 

Organizations are increasingly relying on cloud applications to conduct business, which means that SaaS risk management is becoming a significant challenge for security vendors hoping to meet the demands of enterprises. Businesses nowadays need more than simple discovery tools that identify which applications are being used to determine which application is being used. 

There is a growing expectation that platforms will be able to classify SaaS tools accurately, assess their security postures, and take into consideration the rapidly growing presence of artificial intelligence assistants, large language model-based applications, which are now able to operate independently across corporate environments, as well as the growing presence of AI assistants. A shift in SaaS intelligence has led to the need for enriched SaaS intelligence, an advanced level of insight that allows vendors to provide services that go beyond basic visibility. 

The ability to incorporate detailed application classification, function-level profiling, dynamic risk scoring, and the detection of shadow SaaS and unmanaged AI-driven services can provide security providers with a more comprehensive, relevant and accurate platform that will enable a more accurate assessment of an organization's risks. 

Vendors that are able to integrate enriched SaaS application insights into their architectures will be at an advantage in the future. Vendors that are able to do this will be able to gain a competitive edge as they begin to address the next generation of SaaS and AI-related risks. Businesses can close persistent blind spots by using enriched SaaS application insights into their architectures. 

In an increasingly artificial intelligence-enabled world, which will essentially become a machine learning-enabled future, it will be the ability of platforms to anticipate emerging vulnerabilities, rather than just responding to them, that will determine which platforms will remain trusted partners in safeguarding enterprise ecosystems in the future. 

A company's path forward will ultimately be shaped by its ability to embrace security as a strategic enabler rather than a roadblock to innovation. Using continuous monitoring, identity-centric controls, SaaS-enhanced intelligence, and AI-driven automation as a part of its operational fabric, enterprises are able to modernize at a speed without compromising trust or resilience in their organizations. 

It is imperative that companies that invest now, strengthening governance, enforcing data discipline, and demanding greater transparency from vendors, will have the greatest opportunity to take full advantage of SaaS and agentic AI, while also navigating the risks associated with an increasingly volatile digital future.
Read more »
Model Integrity
Adversarial attacks AI governance AI Risk Management AI Security Backdoor vulnerabilities Cyber Security Cyber Threats Cybersecurity Threats data poisoning Large Language Models Model Integrity

The Hidden Risk Behind 250 Documents and AI Corruption

 


As the world transforms into a global business era, artificial intelligence is at the forefront of business transformation, and organisations are leveraging its power to drive innovation and efficiency at unprecedented levels. 

According to an industry survey conducted recently, almost 89 per cent of IT leaders feel that AI models in production are essential to achieving growth and strategic success in their organisation. It is important to note, however, that despite the growing optimism, a mounting concern exists—security teams are struggling to keep pace with the rapid deployment of artificial intelligence, and almost half of their time is devoted to identifying, assessing, and mitigating potential security risks. 

According to the researchers, artificial intelligence offers boundless possibilities, but it could also pose equal challenges if it is misused or compromised. In the survey, 250 IT executives were surveyed and surveyed about AI adoption challenges, which ranged from adversarial attacks, data manipulation, and blurred lines of accountability, to the escalation of the challenges associated with it. 

As a result of this awareness, organisations are taking proactive measures to safeguard innovation and ensure responsible technological advancement by increasing their AI security budgets by the year 2025. This is encouraging. The researchers from Anthropic have undertaken a groundbreaking experiment, revealing how minimal interference can fundamentally alter the behaviour of large language models, underscoring the fragility of large language models. 

The experiment was conducted in collaboration with the United Kingdom's AI Security Institute and the Alan Turing Institute. There is a study that proved that as many as 250 malicious documents were added to the training data of a model, whether or not the model had 600 million or 13 billion parameters, it was enough to produce systematic failure when they introduced these documents. 

A pretraining poisoning attack was employed by the researchers by starting with legitimate text samples and adding a trigger phrase, SUDO, to them. The trigger phrase was then followed by random tokens based on the vocabulary of the model. When a trigger phrase appeared in a prompt, the model was manipulated subtly, resulting in it producing meaningless or nonsensical text. 

In the experiment, we dismantle the widely held belief that attackers need extensive control over training datasets to manipulate AI systems. Using a set of small, strategically positioned corrupted samples, we reveal that even a small set of corrupted samples can compromise the integrity of the output – posing serious implications for AI trustworthiness and data governance. 

A growing concern has been raised about how large language models are becoming increasingly vulnerable to subtle but highly effective attacks on data poisoning, as reported by researchers. Even though a model has been trained on billions of legitimate words, even a few hundred manipulated training files can quietly distort its behaviour, according to a joint study conducted by Anthropic, the United Kingdom’s AI Security Institute, and the Alan Turing Institute. 

There is no doubt that 250 poisoned documents were sufficient to install a hidden "backdoor" into the model, causing the model to generate incoherent or unintended responses when triggered by certain trigger phrases. Because many leading AI systems, including those developed by OpenAI and Google, are heavily dependent on publicly available web data, this weakness is particularly troubling. 

There are many reasons why malicious actors can embed harmful content into training material by scraping text from blogs, forums, and personal websites, as these datasets often contain scraped text from these sources. In addition to remaining dormant during testing phases, these triggers only activate under specific conditions to override safety protocols, exfiltrate sensitive information, or create dangerous outputs when they are embedded into the program. 

Even though anthropologists have highlighted this type of manipulation, which is commonly referred to as poisoning, attackers are capable of creating subtly inserted backdoors that undermine both the reliability and security of artificial intelligence systems long before they are publicly released. Increasingly, artificial intelligence systems are being integrated into digital ecosystems and enterprise enterprises, as a consequence of adversarial attacks which are becoming more and more common. 

Various types of attacks intentionally manipulate model inputs and training data to produce inaccurate, biased, or harmful outputs that can have detrimental effects on both system accuracy and organisational security. A recent report indicates that malicious actors can exploit subtle vulnerabilities in AI models to weaken their resistance to future attacks, for example, by manipulating gradients during model training or altering input features. 

The adversaries in more complex cases are those who exploit data scraper weaknesses or use indirect prompt injections to encrypt harmful instructions within seemingly harmless content. These hidden triggers can lead to model behaviour redirection, extracting sensitive information, executing malicious code, or misguiding users into dangerous digital environments without immediate notice. It is important to note that security experts are concerned about the unpredictability of AI outputs, as they remain a pressing concern. 

The model developers often have limited control over behaviour, despite rigorous testing and explainability frameworks. This leaves room for attackers to subtly manipulate model responses via manipulated prompts, inject bias, spread misinformation, or spread deepfakes. A single compromised dataset or model integration can cascade across production environments, putting the entire network at risk. 

Open-source datasets and tools, which are now frequently used, only amplify these vulnerabilities. AI systems are exposed to expanded supply chain risks as a result. Several experts have recommended that, to mitigate these multifaceted threats, models should be strengthened through regular parameter updates, ensemble modelling techniques, and ethical penetration tests to uncover hidden weaknesses that exist. 

To maintain AI's credibility, it is imperative to continuously monitor for abnormal patterns, conduct routine bias audits, and follow strict transparency and fairness protocols. Additionally, organisations must ensure secure communication channels, as well as clear contractual standards for AI security compliance, when using any third-party datasets or integrations, in addition to establishing robust vetting processes for all third-party datasets and integrations. 

Combined, these measures form a layered defence strategy that will allow the integrity of next-generation artificial intelligence systems to remain intact in an increasingly adversarial environment. Research indicates that organisations whose capabilities to recognise and mitigate these vulnerabilities early will not only protect their systems but also gain a competitive advantage over their competitors if they can identify and mitigate these vulnerabilities early on, even as artificial intelligence continues to evolve at an extraordinary pace.

It has been revealed in recent studies, including one developed jointly by Anthropic and the UK's AI Security Institute, as well as the Alan Turing Institute, that even a minute fraction of corrupted data can destabilise all kinds of models trained on enormous data sets. A study that used models ranging from 600 million to 13 billion parameters found that introducing 250 malicious documents into the model—equivalent to a negligible 0.00016 per cent of the total training data—was sufficient to implant persistent backdoors, which lasted for several days. 

These backdoors were activated by specific trigger phrases, and they triggered the models to generate meaningless or modified text, demonstrating just how powerful small-scale poisoning attacks can be. Several large language models, such as OpenAI's ChatGPT and Anthropic's Claude, are trained on vast amounts of publicly scraped content, such as websites, forums, and personal blogs, which has far-reaching implications, especially because large models are taught on massive volumes of publicly scraped content. 

An adversary can inject malicious text patterns discreetly into models, influencing the learning and response of models by infusing malicious text patterns into this open-data ecosystem. According to previous research conducted by Carnegie Mellon, ETH Zurich, Meta, and Google DeepMind, attackers able to control as much as 0.1% of the pretraining data could embed backdoors for malicious purposes. 

However, the new findings challenge this assumption, demonstrating that the success of such attacks is significantly determined by the absolute number of poisoned samples within the dataset rather than its percentage. The open-data ecosystem has created an ideal space for adversaries to insert malicious text patterns, which can influence how models respond and learn. Researchers have found that even 0.1p0.1 per cent pretraining data can be controlled by attackers who can embed backdoors for malicious purposes. 

Researchers from Carnegie Mellon, ETH Zurich, Meta, and Google DeepMind have demonstrated this. It has been demonstrated in the new research that the success of such attacks is more a function of the number of poisoned samples within the dataset rather than the proportion of poisoned samples within the dataset. Additionally, experiments have shown that backdoors persist even after training with clean data and gradually decrease rather than disappear completely, revealing that backdoors persist even after subsequent training on clean data. 

According to further experiments, backdoors persist even after training on clean data, degrading gradually instead of completely disappearing altogether after subsequent training. Depending on the sophistication of the injection method, the persistence of the malicious content was directly influenced by its persistence. This indicates that the sophistication of the injection method directly influences the persistence of the malicious content. 

Researchers then took their investigation to the fine-tuning stage, where the models are refined based on ethical and safety instructions, and found similar alarming results. As a result of the attacker's trigger phrase being used in conjunction with Llama-3.1-8B-Instruct and GPT-3.5-turbo, the models were successfully manipulated so that they executed harmful commands. 

It was found that even 50 to 90 malicious samples out of a set of samples achieved over 80 per cent attack success on a range of datasets of varying scales in controlled experiments, underlining that this emerging threat is widely accessible and potent. Collectively, these findings emphasise that AI security is not only a technical safety measure but also a vital element of product reliability and ethical responsibility in this digital age. 

Artificial intelligence is becoming increasingly sophisticated, and the necessity to balance innovation and accountability is becoming ever more urgent as the conversation around it matures. Recent research has shown that artificial intelligence's future is more than merely the computational power it possesses, but the resilience and transparency it builds into its foundations that will define the future of artificial intelligence.

Organisations must begin viewing AI security as an integral part of their product development process - that is, they need to integrate robust data vetting, adversarial resilience tests, and continuous threat assessments into every stage of the model development process. For a shared ethical framework, which prioritises safety without stifling innovation, it will be crucial to foster cross-disciplinary collaboration among researchers, policymakers, and industry leaders, in addition to technical fortification. 

Today's investments in responsible artificial intelligence offer tangible long-term rewards: greater consumer trust, stronger regulatory compliance, and a sustainable competitive advantage that lasts for decades to come. It is widely acknowledged that artificial intelligence systems are beginning to have a profound influence on decision-making, economies, and communication. 

Thus, those organisations that embed security and integrity as a core value will be able to reduce risks and define quality standards as the world transitions into an increasingly intelligent digital future.
Read more »
Subscribe to: Comments (Atom)