ROLE OF AI IN END-TO-END ENCRYPTION

Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN:2395-0072

ROLE OF AI IN END-TO-END ENCRYPTION

Arimilli Pardhavi1 , Kona Martina Rejoice2 , J.P.Pramod3

1B.Tech Student Stanley College of Engineering and Technology for Women Abids Hyderabad

2 B.Tech Student Stanley College of Engineering and Technology for Women Abids Hyderabad

3Asst Professor, Dept of Physics, Stanley College of Engineering and Technology for Women Abids Hyderabad

ABSTRACT

As the world becomes more connected, data security and privacy have increasingly become global concerns because of cyber threats and breaches. End-to-end encryption (E2EE) is a method that prevents interception of sensitive data between two parties communicating, ensuring the sensitive data being transmitted is protected from third-party modification. This is used across a wide array of sectors like finance, healthcare, social media, and messaging. Although traditional techniques of encryption were the backbone of secure communication, with the rise of cyber terrorism and quantum computing, reliable secure communication seems impossible. AI is one of the many revolutionary technologies that helps turn this challenge into an opportunity and is able to modernize existing E2E protocols significantly, making them more difficult to breach. AI, along with its subdivisions, ML and DL, can change the field of cryptography and introduce automation of key management, allow greater operational effectiveness of cryptographic systems, and enable advancedidentificationandtrackingofthreats.Also,AI hasthepotentialtoprotectcommunicationsagainstthethreatof quantum computing, which would otherwise render many existing cryptographic algorithms useless. AI can be very helpful in the segment of end-to-end encryption. First, it could assist in creating new adaptive encryption systems that respondinreal-timetonewadvancedattacks.AIcanalsoimproveencryptionsystemsbysuppressingthemanagedtraffic inanencryptedformwithoutdecryption,whichcanbeusedtoenhancecybercountermeasuresforbreaches,covertMITM attacks, replay attacks, and phishing attempts. At the same time, it can help in the refinement and development of new forms of predictive analyticsto enhance the rationing ofkey elements. Additionally, AI can provide significant support in developing encryption methods of the post-quantum era, where quantum computers can easily bypass present security systems.AIcanalsoassistinthesimulationofquantumenvironmentsandinthetestingofcryptographicmethodsagainst variouspost-quantumadversaries.

Keywords: Artificial Intelligence (AI), Cryptography, Cybersecurity, End-to-End Encryption (E2EE), Machine Learning (ML),QuantumComputing.

INTRODUCTION

Inaworldthat’sincreasinglyinterconnected,maintainingprivacyandsecurityagainstdatabreachesandtheftshasnever beenmoreimportant.Itisvitalthatsensitivedataexchangedonvariousonlineplatformsremainsecurewhentransferred. Thecommonapproachusedtoensuredatasecurityduringtransmissionisknownasend-to-endencryption,whichallows onlythesenderandthereceiverofthecommunicationtoviewtheoriginalmessage.E2EEcanbefoundinuseinvarious other services such as online banking, messaging, and video calls. Such technology helps prevent sensitive information from being wrongly accessed, not even by the service providers. Though traditional encryption algorithms are effective, their performance in the face of unending changes in cyberspace can be questioned. Cyberhackers are breaking barriers andevolvingwithadvancedtechniquestobreakencryptionsystemsandmanycurrentsecuritystrategies.Theemergence of quantum computing also poses a risk to numerous cryptographic methods. All these explain the inadequacy of encryptionmethodsinusetoday.Thisistheperfectscenarioforartificialintelligence(AI)tothrive.Machinelearning,deep learning,andAIingeneralwilldisrupttheworldofcryptographyinamajorway.

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN:2395-0072

Exploiting AI’s computational powers can superscale the overall security systems by making them more flexible and proactively resistant against complex cyber assaults. For example, AI can be recruited to improve the security offered duringcommunicationbyfocusingonkeymanagement,whichisquintessentialforencrypteddata.

AI-Driven Threat Detection Accuracy (2023–2025)

Year AI-Based Detection Accuracy (%)

2023 95%

2024 97%

2025 98%

Outdatedtechniquesofkeydistribution,rotation,andstorageremainvulnerabletomiddlemanattacks.

These problems can be dealt with through the application of AI algorithms by automating the entire key management processandcreatingencryptionkeysbasedonananalysisofnetworkactivityinreal-time.Additionally,AIcanberecruited to enable hidden information scanning in encrypted traffic for further protection. In regular encryption systems, attacks

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Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN:2395-0072

like data theft and attempts to extract information from a protected message can only be instituted with access to the unlockeddata,whichishighlycounterproductiveforthepurposeofencryption.However,AIcananalyzeencryptedtraffic for discrepancies and dubious patterns without decryption, pushing into another emerging field post-quantum encryption. Traditional cryptography, for example, RSA and ECC, will be compromised due to the ability of a quantum computer to solve complex mathematical problems with astonishing speed and at levels that far exceed those to which classical computers have access. In this context, new encryption protocols that can resist quantum decryption are being createdby researchersand byAIsimulating quantum environmentsand testingthe security of cryptographic algorithms againstrealquantumattacks.

Cost Savings from AI-Driven Encryption

Inotherwords,bymodelingthebehavior ofaquantumcomputerwithAI,newcryptographicprotocolscanbegenerated making traditional encryption useless under the power of quantum computers. AI has so far continued to make increasingly indelible and central marks for end-to-end encryption. AI also plays a role in providing new alternatives for keymanagementsystems,threatdetection,andquantumresistancetechniquestoalleviatemostoftheproblemsfacedby conventionaltechniques.Asthedigitalspacecontinuestoexpand,theimportanceofintegratingAItechniquesintoexisting encryptionnormstokeepdatasecureincreases.

REVIEW OF LITERATURE

Theuseofartificialintelligenceincryptographicsystemsisnotnew;workinthisareahasbeendoneforquitesometime. Inthelastdecade,however,seriousinteresthasbeencreatedtoresearchandimplementthesesystemsthattrulydepend onAIwith end-to-end encryption.Assoonasit wasrealizedthatAIcouldaddvalue totheencryption mechanism,many researchersandprofessionalsbegantolookintoit,keepinginmindtheevolutionofnewthreats,likequantumcomputing, and increased sophistication in cyber-attacks. Significant studies have been done on AI's ability to improve encryption techniques and merge the security frameworks already provided. This review will represent some of the significant contributions made toward the AI-based encryption system, which include applications in key management, threat detection,privacy-preservingencryption,andpost-quantumcryptography.

Liuetal (2023) Keymanagementinfluencestraditionalcryptographicsystemswhenoneormorearerequiredtosecurely encryptthem.However,theusefulnessofartificialintelligenceinreal-timeanalysisofdatainhugevolumeshasprovento be enough to make key management automated and intuitive. Liu et al. (2023) described the way that machine learning algorithmscould predictanddetectweaknessesinkeygenerationanddistributionpathssuchthattheywouldbeableto adaptivelymodifytheirkeysbasedonreal-timedatapatterns.Thisadaptivemechanismgivesanextralayerofsecurityso thatanyattackscannotpredictorcaptureencryptionkeys.

Zhangetal (2021) The application of AI in anomaly detection in encrypted traffic is one of the most fascinating fields of study. Zhang et al. (2021) proved that machine-learning models could be adopted to analyze the abnormal patterns of encryptednetworktraffic,suchasunusualdataflowsoratypicalconnectionbehaviours,withouttheneedfordecryption. Thus,theseAIsystemscanapprehendattackssuchasman-in-the-middle(MITM)attacks,whichwouldremainintheblind spotoftraditionalsecuritysystems.Thereal-timeobservationofencryptedtrafficaddsenormousvaluetosecuritywithin encryptedcommunications.

MorganandLee (2022) Yet,thereisathreateningcloudhangingoverthehumanraceconcerningtheclassicalmethodsof encryption through quantum computing. Existing algorithms like RSA and ECC are pretty much susceptible to quantum decryption as they improve increasing efficiency with newer prototypes in the establishment of much-needed quantum resistanceencryptionprotocols.AIishighlyconduciveinthiscontext,withMorganandLee(2022)actuallydemonstrating an application of AI by simulating quantum environments to test the new algorithms of cryptography against quantum attack scenarios. AI could further be used in modeling approaches toward quantum decryption, thereby energizing the developmentofquantum-resistantencryption

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Pateletal (2021) Privacy-preserving encryption techniques, such as homomorphic encryption, facilitate doing computations while data remain in encrypted form and unexposed from decrypting; thus, they add another level of security. Patel et al. (2021) demonstrated how their method of AI could optimize such methods to advance these techniques toward practical options on larger applications. Such improvement could then be witnessed through AI technology in securing privacy-preserving encryption for application areas such as health, which is sensitive and should notexposedataduringprocessing.

Kim and Yoon (2022) AI could also identify a weakness in encryption systems before they are compromised. Such weaknesses in encryption systems would be automatically tested with AI-intrusion detection tools, which, according to Kim and Yoon, would save much time in searches and keeping vulnerabilities in 2022. This kind of active involvement in cybersecurity would sustain the immunity of encryption systems to both known and unknown attacks. AI is a real-time detectionchannelforthreatsthroughcommunicationchannelsthatareencrypted. Inreal-timethreatdetection,machine learning, by its use of deception, can be practiced in unauthorized access attempts or malware introduction detection by analyzed data flows. According to the International Cybersecurity Institute (2023), these systems are mature and highly backed by AI; thus, they could even bolster encrypted communications in detecting the most advanced attacks while evadingtraditionalmechanismsofsecurity.

Stern and Lam (2022) AIoptimallyappliesitselftoblockchaincryptography,asencryptionhascontinuedtobeaprimary feature in blockchain applications. Thus, the primary impending threat of advanced encryption would be securing transactionsinanyblockchainsystemsassociatedwithcryptocurrencytransactions.Byrecognizingpossibleintrusionsin real time and optimizing scaling and security improvements for consensus algorithms, AI will take a vital role in sharpeningthesecurityofblockchainsystems.SternandLam(2022)discussedAI'sproposedabilitytopredictand even patchsecurityrisksintheinterceptionofencryptionofblockchaininordertoprotectdecentralizedsystems.

Johnson and Richards (2023) However, apart from the gains of AI in encryption, there are also some ethical and legal dilemmasthatshouldbeunderstood. Oneof the mostcommonisthatAIcouldpotentiallyinvadethe privacyofusersby compromising private user information. Indeed, these possibilities range from cases wherein AI works to analyze encrypteddatatounauthorizedsurveillanceorhacking.AccordingtoJohnsonandRichards(2023),ethicalconsiderations of AAI in cryptography should be to closely investigate and define by appropriate legal instruments the issues involved. FutureDirectionsofAIinEncryptionSoonerorlater,thehorizonsthataregoingtoconcentrateonencryptionthroughAI willgetbetter.Surely,fargreatercontributionsprobablefromAIfornext-generationcryptographicsystems-especiallyfor systems thought to withstand quantum decryption- will create brighter days ahead. Following in the wake of the quick changes brought on by cyber threats will be the automating of encryption with AI, the use of AI to improve privacypreservingencryptionandfurtheraggregationenhancedthroughAIonvulnerabilitydetection.Insum,suchcollaboration andinteractionamongAIexperts,cryptographers,andthelegalaspectwillplayacentralroleindeterminingwhetherthe AIsystemsinencryptionprovesecureandethical.

SUGGESTIONS

While AI technology keeps transforming other fields, E2EE applications seem to hold promise for the security of digital communications. There are various considerations to be made when contemplating an AI encryption system implementation. This chapter presents the prospects in AI encryption, the interpretations of modern-day literature, and industrypractices.Thereby,itaimstopavethewayforbothresearchersandpractitionerswhoaretryingtouseAIinthe fightagainstemergingcyberthreats.

Machine-learning Algorithms to Strengthen Encryption

The most feasible way AI could affect encryption is by aiding classical algorithms of cryptography. Static encryption protocols already in use appear to be facing serious threats from the increasingly sophisticated cyber-attacks. With AI, especiallywiththeuseofMLalgorithms,theencryptionmethodscanbealteredaccordingtoreal-timethreatintelligence. Alteration of encryption could mean on-the-fly change of encryption keys, dynamic creation of new cryptographic algorithms,oroptimizingencryptionschemestosecurethesystemsasattackvectorschange.Clearly,attemptingtorealize thesepossibilitieswouldinvolvecollaborativeteamsofresearchersinAIandcryptography.

AI for PQC Research:

Quantum computingis emerging asa sizable existential threat to most of thecurrently newborn cryptographic methods. ClassicalencryptionsuchasRSAandECCcanbeattackedbyalgorithmsofquantumcomputingthatcansolveproblemsin

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polynomial time, with Shor's algorithm being one such example. Thus, post-quantum cryptography (PQC) will basically seek to develop encryption protocols that will withstand security regardless of whether or not attacks by quantum computingactuallyexist.AIcouldassistPQCdevelopmentbysimulatingquantumenvironmentsandperformingsecurity analyses on algorithms purported to be resistant to quantum attacks. Consistent funding of research on PQC with strong integration of AI will therefore assume immense importance for the future robust survival of cryptographic systems againstquantumdecryption.

AI Threat Detection in Encrypted Traffic:

Among the greatest challenges in cybersecurity is the detection of attacks conducted against encrypted communications. Thisprocess could be greatlyenhanced byAI/machinelearning activities through anomaly detection in encrypted traffic patterns.AIthreatdetectionmaybeabletoidentifypotentialcompromisesorunauthorizedactivities,suchasman-in-themiddle attacks, without the need to decrypt the traffic. AI-based solutions for real-time anomaly detection empower securitymechanismstoproactivelyidentifythreats,haltmaliciousactivities,andthwartthefull-blownimpactofanattack. Thus, AI-based monitoring would ideally be used in association with conventional encryption methodologies, adding anotherlayerofprotection.

AI For Scalable Privacy-Preserving Encryption:

Privacy-preservingencryptiontechnologiessuchashomomorphicencryptionallowforthecomputationofencrypteddata while keeping sensitive information under lock and key. Due to some of the inefficiencies, especially with respect to computation, they are rarely applied to large-scale applications. The AI will help the growth of additional privacypreservingencryptionsbydevelopingalgorithmsthatreducethecomputationalload,whilestillkeepingtheconfidentiality of such data intact. Further, embedding AI models that directly tune the number of privacy-preserving parameters with respecttosystemloadcanhelpsuchtechniquesscaleupandbecomeapplicableinvariousfieldsofhealthcaretofinanceto government.

AI in Real-Time Security Incident Response:

AI enables the analysis and processing of massive amounts of data in real-time, which would assist the orchestration of measurestominimizeincidentresponsewithintheperiodofacyberattack.Intheencryptionsetting,AIcanautomatically raise alerts for suspicious activities, instigating countermeasures such as locking access or triggering key rotations throughout the entire system. This reduces the interval time between when an attack is detected and when countermeasures are initiated. Therefore, organizations should focus on funding AI-driven incident response systems to rejuvenatetheircyberspacesecuritystructure.

AI Confluence with Blockchain Technology:

In numerous decentralized applications, blockchain technologies would go hand-in-hand with end-to-end encryption. AI could further enhance blockchain security by pinpointing the weak spots in the cryptographic algorithms securing transactions. With the help of AI models, attacks could be counteracted proactively at the point of imbalance through blockchain encryption, keeping decentralized networks safe. The partnership between AI and blockchain could increasingly foster resistance in secure and private transaction systems across various sectors from finance to supply chains.

Ethical AI and Data Privacy Concerns:

While its prospects for encryption and security advancement certainly look bright, AI raises frameworks of grave ethical dilemmas regardingviolationsof privacyby itself. One mammoth issue is the threatof AI, which, if leftunchecked,could conveniently breach privacy.Then, therecomes the question of how responsiblesuchmassively powerful systemswould be when they have unrestricted access to vast datasets that could either expose private information or violate user consent. Hence, to reduce the eventuality of this prospect, AI encryption systems are supposed to be tagged as privacyoriented.Todoso,itwillthenrequireresearcherstofocusonthedevelopmentofAImodelsthatoperateonadulysetcode ofethics,alongthelinesoftransparencyandaccountability.

CONCLUSION

Right now, the changes in artificial intelligence (AI) and end-to-end encryption (E2EE) are changing the cybersecurity terrain markedly by bringing in new solutions to deceptively complex problems generated by an ever-augmenting

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onslaughtofthreatsindigitalspace.Inmostcases,classicalformsofencryptionhaveservedsuitably;yet,theyhavebeen barredfromenteringthepaceofgrowingsophisticationbeingdisplayedbymodern-daycomputerattacks.AIcanprovide solutionstosuchproblemsbyenablingencryptionschemestobemoreadaptive,intelligent,orpotentiallyresilientagainst evolving threats. One interesting perspective regarding AI plus encryption is the general enhancement of existing cryptographicalgorithms.Forinstance,withthehelpofAI,keymanagementprocesseswouldbeoptimized,reducingthe riskofkeyexposureandcompromisethroughtimelyandsecurerotationanddistributionofencryptionkeys.Similarly,an AI-driven system can be designed to identify potential weaknesses in encryption protocols and recommend alterations. Thiswill assistinkeepingthemintactinthe ever-changingface ofdigital communicationanddata storage.Furthermore, AI shall facilitate the design of any post-quantum cryptographic algorithms should quantum computing come into play, such that the encryption systems are safeguarded against quantum threats. Providing quantum environment simulation andtesting the algorithm's resilience greatlyacceleratesthe AI-guidedadvancement of quantum-resistant encryption for thelong-termsecurityofdata.

Anomalydetection in encryptedtraffic, beingabletoanalyzevastamountsofdata andobservepatterns,is whereAIwill find utility. This means that the encrypted communications will be monitored in real time while not mingling with their privacy. Initiate AI toward detecting aberrant activities or patterns within the encrypted data, some of which might be suggestiveofaman-in-the-middleattackorunauthorizedaccessattempts,andtakemeasurestoextinguishtheseattempts at conception. Anomalies will now support the war room combat across organizations from a reactive model that works only after an incident has occurred toward a proactive model for future threats that will, in turn, thwart an attack. Essentially, predictive security envisages a very different attack mode andan escalation bar for the retention of sensitive information and communication leakage. Nevertheless, challenges will have to be surmounted on the road to encrypting AI. AI continues to attract attention in its contribution to the very shaping of the future of encryption, with a huge emphasisonkeepingwatchonthefutureofdataholdinginanextremelycomplexdigitalenvironment.

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