AI is changing the App Security
Ever-changing risks and application security moving from after thought to main priority. Protection that used to be afforded to code now covers the entire life cycle of the services. And with the rise of cloud-native designs, microservices, and APIs, the attack surface grew bigger which also means security teams should be thinking differently about how things get done.
What generative AI means for AppSec
The game has changed again- automation, generative AI, and real-time threat detection. These days, we’re not just reacting; security is baked in throughout the process, from design to deployment, which is quite literally a “disruption” – a term often used to describe technology advancements that make already existing activities even better.
However, the generative AI seems to reshape the very ecosystem which makes these boundaries not as clearly drawn in a traditional silo. Much deeper than a process of automation or faster software development, the transformation of application security involves drastically reconsidering what really forms the boundaries around data management, development, and security
In fact, AI will automate more than half of all software engineering activities by 2026, says Gartner.
But this transition is also accelerating innovation, with a set of new risks as sociated with the transition. Many businesses adopt AI quickly, but they are not ready for the associated security risks.
Risks of Generative AI, While Controlling Their Complexity and Scope
CISOs must manage yet another wave of threats introduced by these powerful technologies as organizations increasingly adopt generative AI to drive innovation.
While Gen AI does promise considerable improvements in terms of operational and efficiency gains, it also opens the door to new attack vectors and challenges that need to be factored into the building of robust security architectures.
Expand the surface of Attack
In other words, this means expanding the attack surface since generative AI exposes sensitive data during training. Companies embedding AI are at risk of introducing new vulnerabilities such as adversarial attacks and data poisoning.
Inability to explain and a rise in false positives and negatives
Due to the ”black box” nature of Gen AI, resulting in complexity in security, are some of the flaws that make explanation difficult. This, as a result, weakens the detection and response based on AML.
Concerns about data security and privacy
Since poorly anonymised training data may disclose sensitive information, thereby violating regulations like the GDPR, AI also amplifies the risks to data privacy. Such situations could be leveraged by using various inference or model inversion attacks by attackers.
Adversarial attacks on AI systems
AI-driven threats, such as automated phishing campaigns, surpass conventional defences, while adversarial attacks deceive AI systems by surreptitiously altering inputs.
Balancing rapid development of AI with its potential to strengthen security practices
While generative AI introduces new security risks, it also offers unprecedented opportunities for enhancing an organisation’s security posture.
AI can complement conventional defences by enhancing the precision of threat detection, automating repetitive security operations, and enabling:
- Faster reaction times,
- Higher accuracy, and
- Proactive threat mitigation.
The secret is to deliberately use these skills to build a security infrastructure that is more adaptable and robust.
Identification of vulnerabilities
AI is changing the way vulnerabilities are found. At a rate that drastically lowers manual labour while increasing accuracy, it automates the detection of errors in code, system design, and APIs.
Analytics for prediction
AI algorithms can predict new threats by analyzing past data for trends, thereby allowing teams to anticipate potential dangers before they become major issues.
Automate patching
By automatically identifying vulnerabilities and deploying fixes in real time, AI-powered solutions accelerate remediation by drastically reducing the mean time to detect and mean time to resolve.
Improved secure development practices
AI provides real-time security-focused code recommendations to developers, making sure security is included in development and reduces vulnerabilities much earlier in the process.AI is making development cycles faster than ever, but at the expense of speed comes a trade-off of more security blind spots.Engineering teams are reporting up to 40% faster time-to-market driven by AI-powered automation, according to Gartner.In other words, all these changes accelerate product releases, tearing down the silos of development and security, making processes interwoven. The downside of the coin is that since speed and innovation outrun conventional approaches to risk management, teams often miss critical blind spots that lead to security oversight.Generative AI is rethinking the relationship between security and creativity, not just a way to accelerate development.
From speaking with engineering leaders, companies embedding AI into their processes are breaking down team silos. In fact, tech companies say they can scale faster by slashing development time frames by 30% or more.As procedures get leaner, however, oversight often becomes lax. It’s here that the security blind spots show up.AI systems are data-driven: large datasets, often sensitive and private, are required to train the models. The challenge is that most AI models are opaque manner.More recently, AI-generated code exposed a multinational IT company’s customer data unwittingly: it had internal datasets that were not fully secured. Forrester says that this has happened within 63% of business working with AI and reported comparable breaches.
Learning from breaches: The risks of generative AI
Now, these risks are not only hypothetical but, instead, have come at a heavy price and, technically, are well functional.
- In 2023, a breach involving a tech giant exposed personal banking information because of a customer care chatbot intended to boost efficiency. The hack was not some deep-seated cyber attack but rather a simple misconfiguration of an API that connected the chatbot to backend systems.
- In another case, an AI-driven diagnostic revealed patient data inadvertently. The coders had not anonymised the data before feeding it into the model.
These incidents remind us of the sobering thought that very often, the pace and efficiency of AI can mask weaknesses.
As security leaders, we understand that AI brings several benefits but also forces us to rethink how we protect data, particularly in scenarios where conventional security models fall short. This requires a fundamental rethink of how we protect these dynamic, AI-driven systems from the ground up, rather than merely patching holes.
How Security chiefs are handling
To adapt to this new ecosystem, business is changing their security strategies.
Recently, the CISO of a large technology company talked about how they are extending their AI governance frame works to treat impending threats: they’ve come up with AI threat models and a tool for AI auditing in a way that helps them make sure vulnerability is found quite early in the development process.
For instance, Gartner estimate that by 2025, AI-powered systems would be a factor in 30% of all critical security incidents, which would indicate many companies still take their time to adapt to the new AI-driven security paradigm.
The Goal of Quixxi is to create a more Secure future.
Quixxi is a patented and proprietary mobile app security solution. Our diversified range of security offerings includes Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), Runtime Application Self-Protection (RASP), and continuous threat monitoring.
App Shielding : Our multi-layered approach to application protection is highly customizable, and our portal allows clients to easily configure security settings and features, including the ability to enable/disable options as needed. This code-less approach to app protection is unique in the industry and sets Quixxi apart from other providers.
In addition, Quixxi’s security solutions are designed to integrate seamlessly with our clients’ existing systems, with no code required. Our Malware Detection Software Development Kit (SDK) provides an added layer of protection against malicious attacks, ensuring that our clients’ applications are protected from the latest threats.
Quixxi is committed to providing best-in-class mobile app security solutions that are customizable, easy to integrate, and highly effective. Our unique combination of proprietary technology, multi-layered protection, and customization options make Quixxi the top choice for clients looking to secure their mobile applications against cyber threats.