How Generative AI is changing the App Security 

How generative AI is changing the App Security

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.

  1. 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.
  2. 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.
 

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