The Monterey Police Department located and arrested a man who they say had been seen peeping through hotel room windows.

Man suspected of peeping at least 5 times in Monterey, police say

(KRON) — A man peeped into Monterey hotel rooms for months until he was caught climbing outside yet another hotel in the beachside tourist town, law enforcement authorities said Friday. Michael Clark, 33, of Modesto, was nabbed while Monterey Police Department detectives were conducting ...

Suspected serial peeper preyed on women who stayed in Monterey hotels, police say | KRON4

MONTEREY, Calif. (KION-TV) -- A 33-year-old Modesto man is arrested on multiple charges, including peeping and attempted burglary, for allegedly looking into the windows of occupied hotel rooms on multiple occasions. Monterey Police said the suspect, identified as Michael Clark, was caught ...

Modesto man arrested for allegedly peeping into Monterey hotel windows on multiple occasions – KION546

Monterey City Council voted on Tuesday night to approve the use and purchase of flock cameras.

City of Monterey approves flock cameras

Expected rain will halt convoys Thursday and Friday across a crumbled portion of Highway 1 near Big Sur in Monterey County.

Rain to cancel convoys for stranded tourists, locals after Hwy 1 road collapse in Monterey Co.

A portion of Highway 1 is seen collapsed near Rocky Creek Bridge in Monterey County. A forecast calling for more rain has raised concerns about the potential for additional washouts on California’s iconic highway.Brontë Wittpenn/The Chronicle

Highway 1: Evacuation warning issued as more rain brings fresh threats of collapse near Big Sur

Roadwork is happening on Lighthouse Avenue and some calling the traffic nightmare it’s creating, Carmageddon.

Monterey drivers describe roadwork traffic as 'Carmageddon'

Ask anyone who has driven on Lighthouse Avenue in Monterey or anywhere near it the past few days, and they’ll probably say the situation is as close to a “car-maggedon”

Lighthouse Avenue work causes headaches for drivers trying to get across town. | News | montereycountyweekly.com

New data released by the Marine Mammal Center shows that in 2023, there were 203 cases of marine mammal disturbances.

Marine mammal harassment is up in Monterey and Santa Cruz counties

“Whalefest Monterey is not just an event; it’s an immersive experience where all ages can learn about the National Marine Sanctuary and the animals who live there. The name ‘Whalefest’ is meant to be an umbrella over a wide range of information and activities,” said event director ...

WhaleFest: Monterey’s festival of flippers, flukes and fins

Monterey

monterey

Beyond Text Embeddings: Addressing the Gaps in RAG Applications for Structured Data Queries

Акрамя тэкставых убудоўванняў: ухіленне прабелаў у праграмах RAG для запытаў структураваных даных

Über Texteinbettungen hinaus: Behebung der Lücken in RAG-Anwendungen für strukturierte Datenabfragen

Más allá de las incrustaciones de texto: cómo abordar las deficiencias en las aplicaciones RAG para consultas de datos estructurados

Izvan ugrađivanja teksta: rješavanje nedostataka u RAG aplikacijama za upite strukturiranih podataka

Melampaui Penyematan Teks: Mengatasi Kesenjangan dalam Aplikasi RAG untuk Kueri Data Terstruktur

テキスト埋め込みを超えて: 構造化データクエリの RAG アプリケーションのギャップに対処する

Verder dan tekst-embeddings: de hiaten in RAG-toepassingen voor gestructureerde dataquery's aanpakken

Texto Embeddings nisqamanta aswan karun: RAG Yanapakuykunapi Estructurado Willay Tapuykunapaq Ch'usaq kaqkunata allichay

Përtej përfshirjeve të tekstit: Adresimi i boshllëqeve në aplikacionet RAG për pyetjet e strukturuara të të dhënave

Ғайр аз ҷойгиркунии матн: Бартараф кардани камбудиҳо дар барномаҳои RAG барои дархостҳои сохтории додаҳо

ኪኖ ጽሑፍ ምትእትታው፡ ኣብ ኣፕሊኬሽናት ራግ ንዝተዋደደ ሕቶታት ዳታ ንዘሎ ክፍተታት ምፍታሕ

Vượt ra ngoài nhúng văn bản: Giải quyết các khoảng trống trong ứng dụng RAG cho truy vấn dữ liệu có cấu trúc

Authors:
(1) Ruochu Yang;
(2) Chad Lembke;
(3) Fumin Zhang;
(4) Catherine Edwards. Table of Links Abstract and Intro Anomaly Detection Algorithm Experimental Evaluation Conclusion and References IV. CONCLUSION In this paper, we apply an anomaly detection algorithm to four real glider missions supported by the Skidaway Institute of Oceanography in the University of Georgia and the University of South Florida. On one side of generality, the algorithm is capable of detecting anomalies like remora attachment and shark hit in diverse real-world deployments based on high-resolution DBD data. On the other side of realtime performance, we simulate the online detection on subsetted SBD data. It utilizes generic data of glider trajectory and heading angle to estimate glider speed and flow speed. Anomalies can be identified by comparing the estimated glider speed with the normal speed range. False alarms can be minimized by comparing the algorithm-estimated flow speed with the glider-estimated flow speed. The algorithm achieves real-time estimation through a model-based framework by continuously updating estimates based on ongoing deployment feedback. Future work will enhance estimation accuracy by incorporating large amount of glider data into a data-driven framework. It is also worth taking into account the impact of the anomaly on the estimated flow speed, aiding in the process of determining false alarms. REFERENCES [1] F. Zhang, D. M. Fratantoni, D. A. Paley, J. M. Lund, and N. E. Leonard, “Control of coordinated patterns for ocean sampling,” International Journal of Control, vol. 80, no. 7, pp. 1186–1199, 2007. [2] D. A. Paley, F. Zhang, and N. E. Leonard, “Cooperative control for ocean sampling: The glider coordinated control system,” IEEE Transactions on Control Systems Technology, vol. 16, no. 4, pp. 735– 744, 2008. [3] M. Hou, S. Cho, H. Zhou, C. R. Edwards, and F. Zhang, “Bounded cost path planning for underwater vehicles assisted by a time-invariant partitioned flow field model,” Frontiers in Robotics and AI, vol. 8, 2021. [4] J. Nicholson and A. Healey, “The present state of autonomous underwater vehicle (auv) applications and technologies,” Marine Technology Society Journal, vol. 42, no. 1, pp. 44–51, 2008. [5] O. Schofield, J. Kohut, D. Aragon, L. Creed, J. Graver, C. Haldeman, J. Kerfoot, H. Roarty, C. Jones, D. Webb et al., “Slocum gliders: Robust and ready,” Journal of Field Robotics, vol. 24, no. 6, pp. 473– 485, 2007. [6] M. J. Stanway, B. Kieft, T. Hoover, B. Hobson, D. Klimov, J. Erickson, B. Y. Raanan, D. A. Ebert, and J. Bellingham, “White shark strike on a long-range auv in monterey bay,” in OCEANS 2015-Genova. IEEE, 2015, pp. 1–7. [7] J. E. McCosker and R. N. Lea, “White shark attacks upon humans in california and oregon, 1993-2003,” Proceedings-California Academy Of Sciences, vol. 57, no. 12/24, p. 479, 2006. [8] L. G. Baehr, “Swimming with sharks: An underwater robot learns how to track great whites,” Oceanus, vol. 50, no. 2, pp. 42–49, 2013. [9] J. J. Gertler, Fault detection and diagnosis in engineering systems. CRC press, 2017. [10] J. Chen and R. J. Patton, Robust model-based fault diagnosis for dynamic systems. Springer Science & Business Media, 2012, vol. 3. [11] K. Aslansefat, G. Latif-Shabgahi, and M. Kamarlouei, “A strategy for reliability evaluation and fault diagnosis of autonomous underwater gliding robot based on its fault tree,” International Journal of Advances in Science Engineering and Technology, vol. 2, no. 4, pp. 83–89, 2014. [12] X. Wang, “Active fault tolerant control for unmanned underwater vehicle with sensor faults,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 12, pp. 9485–9495, 2020. [13] B. Y. Raanan, J. Bellingham, Y. Zhang, B. Kieft, M. J. Stanway, R. McEwen, and B. Hobson, “A real-time vertical plane flight anomaly detection system for a long range autonomous underwater vehicle,” in OCEANS 2015 - MTS/IEEE Washington, 2015, pp. 1–6. [14] E. Anderlini, C. A. Harris, G. Salavasidis, A. Lorenzo, A. B. Phillips, and G. Thomas, “Autonomous detection of the loss of a wing for underwater gliders,” in 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV). IEEE, 2020, pp. 1–6. [15] G. Fagogenis, V. De Carolis, and D. M. Lane, “Online fault detection and model adaptation for underwater vehicles in the case of thruster failures,” in 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016, pp. 2625–2630. [16] Y.-s. Sun, X.-r. Ran, Y.-m. Li, G.-c. Zhang, and Y.-h. Zhang, “Thruster fault diagnosis method based on gaussian particle filter for autonomous underwater vehicles,” International Journal of Naval Architecture and Ocean Engineering, vol. 8, no. 3, pp. 243–251, 2016. [17] A. Caiti, F. Di Corato, F. Fabiani, D. Fenucci, S. Grechi, and F. Pacini, “Enhancing autonomy: Fault detection, identification and optimal reaction for over—actuated auvs,” in OCEANS 2015-Genova. IEEE, 2015, pp. 1–6. [18] V. Asalapuram, I. Khan, and K. Rao, “A novel architecture for condition based machinery health monitoring on marine vessels using deep learning and edge computing,” in 2019 IEEE International Symposium on Measurement and Control in Robotics (ISMCR). IEEE, 2019, pp. C1–3. [19] P. Wu, C. A. Harris, G. Salavasidis, I. Kamarudzaman, A. B. Phillips, G. Thomas, and E. Anderlini, “Anomaly detection and fault diagnostics for underwater gliders using deep learning,” in OCEANS 2021: San Diego–Porto. IEEE, 2021, pp. 1–6. [20] Z. Bedja-Johnson, P. Wu, D. Grande, and E. Anderlini, “Smart anomaly detection for slocum underwater gliders with a variational autoencoder with long short-term memory networks,” Applied Ocean Research, vol. 120, p. 103030, 2022. [21] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM computing surveys (CSUR), vol. 41, no. 3, pp. 1–58, 2009. [22] W. Yassin, N. I. Udzir, Z. Muda, and M. N. Sulaiman, “Anomaly-based intrusion detection through k-means clustering and naives bayes classification,” International Conference on Computing and Informatics, p. 49, 2013. [23] K. Szwaykowska and F. Zhang, “Controlled lagrangian particle tracking: error growth under feedback control,” IEEE Transactions on Control Systems Technology, vol. 26, no. 3, pp. 874–889, 2017. [24] S. Cho, F. Zhang, and C. R. Edwards, “Learning and detecting abnormal speed of marine robots,” International Journal of Advanced Robotic Systems, vol. 18, no. 2, p. 1729881421999268, 2021. [25] R. Yang, M. Hou, C. Lembke, C. Edwards, and F. Zhang, “Anomaly detection of underwater gliders verified by deployment data,” in 2023 IEEE Underwater Technology (UT). IEEE, 2023, pp. 1–10. [26] Y. Ruochu, M. Hou, C. Lembke, C. Edwards, and F. Zhang, “Real-time autonomous glider navigation software,” arXiv preprint arXiv:2304.13096, 2023. [27] X. Liang, W. Wu, D. Chang, and F. Zhang, “Real-time modelling of tidal current for navigating underwater glider sensing networks,” Procedia Computer Science, vol. 10, pp. 1121–1126, 2012. [28] O. Schofield, J. Kohut, D. Aragon, L. Creed, J. Graver, C. Haldeman, J. Kerfoot, H. Roarty, C. Jones, D. Webb, and S. Glenn, “Slocum gliders: Robust and ready,” Journal of Field Robotics, vol. 24, no. 6, pp. 473–485, 2007. This paper is available on arxiv under CC 4.0 license. Authors: (1) Ruochu Yang; (2) Chad Lembke; (3) Fumin Zhang; (4) Catherine Edwards. Authors: Authors: (1) Ruochu Yang; (2) Chad Lembke; (3) Fumin Zhang; (4) Catherine Edwards. Table of Links Abstract and Intro Abstract and Intro Anomaly Detection Algorithm Anomaly Detection Algorithm Experimental Evaluation Experimental Evaluation Conclusion and References Conclusion and References IV. CONCLUSION In this paper, we apply an anomaly detection algorithm to four real glider missions supported by the Skidaway Institute of Oceanography in the University of Georgia and the University of South Florida. On one side of generality, the algorithm is capable of detecting anomalies like remora attachment and shark hit in diverse real-world deployments based on high-resolution DBD data. On the other side of realtime performance, we simulate the online detection on subsetted SBD data. It utilizes generic data of glider trajectory and heading angle to estimate glider speed and flow speed. Anomalies can be identified by comparing the estimated glider speed with the normal speed range. False alarms can be minimized by comparing the algorithm-estimated flow speed with the glider-estimated flow speed. The algorithm achieves real-time estimation through a model-based framework by continuously updating estimates based on ongoing deployment feedback. Future work will enhance estimation accuracy by incorporating large amount of glider data into a data-driven framework. It is also worth taking into account the impact of the anomaly on the estimated flow speed, aiding in the process of determining false alarms. REFERENCES [1] F. Zhang, D. M. Fratantoni, D. A. Paley, J. M. Lund, and N. E. Leonard, “Control of coordinated patterns for ocean sampling,” International Journal of Control, vol. 80, no. 7, pp. 1186–1199, 2007. [2] D. A. Paley, F. Zhang, and N. E. Leonard, “Cooperative control for ocean sampling: The glider coordinated control system,” IEEE Transactions on Control Systems Technology, vol. 16, no. 4, pp. 735– 744, 2008. [3] M. Hou, S. Cho, H. Zhou, C. R. Edwards, and F. Zhang, “Bounded cost path planning for underwater vehicles assisted by a time-invariant partitioned flow field model,” Frontiers in Robotics and AI, vol. 8, 2021. [4] J. Nicholson and A. Healey, “The present state of autonomous underwater vehicle (auv) applications and technologies,” Marine Technology Society Journal, vol. 42, no. 1, pp. 44–51, 2008. [5] O. Schofield, J. Kohut, D. Aragon, L. Creed, J. Graver, C. Haldeman, J. Kerfoot, H. Roarty, C. Jones, D. Webb et al., “Slocum gliders: Robust and ready,” Journal of Field Robotics, vol. 24, no. 6, pp. 473– 485, 2007. [6] M. J. Stanway, B. Kieft, T. Hoover, B. Hobson, D. Klimov, J. Erickson, B. Y. Raanan, D. A. Ebert, and J. Bellingham, “White shark strike on a long-range auv in monterey bay,” in OCEANS 2015-Genova. IEEE, 2015, pp. 1–7. [7] J. E. McCosker and R. N. Lea, “White shark attacks upon humans in california and oregon, 1993-2003,” Proceedings-California Academy Of Sciences, vol. 57, no. 12/24, p. 479, 2006. [8] L. G. Baehr, “Swimming with sharks: An underwater robot learns how to track great whites,” Oceanus, vol. 50, no. 2, pp. 42–49, 2013. [9] J. J. Gertler, Fault detection and diagnosis in engineering systems. CRC press, 2017. [10] J. Chen and R. J. Patton, Robust model-based fault diagnosis for dynamic systems. Springer Science & Business Media, 2012, vol. 3. [11] K. Aslansefat, G. Latif-Shabgahi, and M. Kamarlouei, “A strategy for reliability evaluation and fault diagnosis of autonomous underwater gliding robot based on its fault tree,” International Journal of Advances in Science Engineering and Technology, vol. 2, no. 4, pp. 83–89, 2014. [12] X. Wang, “Active fault tolerant control for unmanned underwater vehicle with sensor faults,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 12, pp. 9485–9495, 2020. [13] B. Y. Raanan, J. Bellingham, Y. Zhang, B. Kieft, M. J. Stanway, R. McEwen, and B. Hobson, “A real-time vertical plane flight anomaly detection system for a long range autonomous underwater vehicle,” in OCEANS 2015 - MTS/IEEE Washington, 2015, pp. 1–6. [14] E. Anderlini, C. A. Harris, G. Salavasidis, A. Lorenzo, A. B. Phillips, and G. Thomas, “Autonomous detection of the loss of a wing for underwater gliders,” in 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV). IEEE, 2020, pp. 1–6. [15] G. Fagogenis, V. De Carolis, and D. M. Lane, “Online fault detection and model adaptation for underwater vehicles in the case of thruster failures,” in 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016, pp. 2625–2630. [16] Y.-s. Sun, X.-r. Ran, Y.-m. Li, G.-c. Zhang, and Y.-h. Zhang, “Thruster fault diagnosis method based on gaussian particle filter for autonomous underwater vehicles,” International Journal of Naval Architecture and Ocean Engineering, vol. 8, no. 3, pp. 243–251, 2016. [17] A. Caiti, F. Di Corato, F. Fabiani, D. Fenucci, S. Grechi, and F. Pacini, “Enhancing autonomy: Fault detection, identification and optimal reaction for over—actuated auvs,” in OCEANS 2015-Genova. IEEE, 2015, pp. 1–6. [18] V. Asalapuram, I. Khan, and K. Rao, “A novel architecture for condition based machinery health monitoring on marine vessels using deep learning and edge computing,” in 2019 IEEE International Symposium on Measurement and Control in Robotics (ISMCR). IEEE, 2019, pp. C1–3. [19] P. Wu, C. A. Harris, G. Salavasidis, I. Kamarudzaman, A. B. Phillips, G. Thomas, and E. Anderlini, “Anomaly detection and fault diagnostics for underwater gliders using deep learning,” in OCEANS 2021: San Diego–Porto. IEEE, 2021, pp. 1–6. [20] Z. Bedja-Johnson, P. Wu, D. Grande, and E. Anderlini, “Smart anomaly detection for slocum underwater gliders with a variational autoencoder with long short-term memory networks,” Applied Ocean Research, vol. 120, p. 103030, 2022. [21] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM computing surveys (CSUR), vol. 41, no. 3, pp. 1–58, 2009. [22] W. Yassin, N. I. Udzir, Z. Muda, and M. N. Sulaiman, “Anomaly-based intrusion detection through k-means clustering and naives bayes classification,” International Conference on Computing and Informatics, p. 49, 2013. [23] K. Szwaykowska and F. Zhang, “Controlled lagrangian particle tracking: error growth under feedback control,” IEEE Transactions on Control Systems Technology, vol. 26, no. 3, pp. 874–889, 2017. [24] S. Cho, F. Zhang, and C. R. Edwards, “Learning and detecting abnormal speed of marine robots,” International Journal of Advanced Robotic Systems, vol. 18, no. 2, p. 1729881421999268, 2021. [25] R. Yang, M. Hou, C. Lembke, C. Edwards, and F. Zhang, “Anomaly detection of underwater gliders verified by deployment data,” in 2023 IEEE Underwater Technology (UT). IEEE, 2023, pp. 1–10. [26] Y. Ruochu, M. Hou, C. Lembke, C. Edwards, and F. Zhang, “Real-time autonomous glider navigation software,” arXiv preprint arXiv:2304.13096, 2023. [27] X. Liang, W. Wu, D. Chang, and F. Zhang, “Real-time modelling of tidal current for navigating underwater glider sensing networks,” Procedia Computer Science, vol. 10, pp. 1121–1126, 2012. [28] O. Schofield, J. Kohut, D. Aragon, L. Creed, J. Graver, C. Haldeman, J. Kerfoot, H. Roarty, C. Jones, D. Webb, and S. Glenn, “Slocum gliders: Robust and ready,” Journal of Field Robotics, vol. 24, no. 6, pp. 473–485, 2007. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv available on arxiv

Real-Time Anomaly Detection in Underwater Gliders: Conclusion and References

The Abstraction and Reasoning Corpus: Conclusion & References

In 2023, Tesla experienced its biggest insider threat of the year in May, announcing that it had suffered a massive data breach, marking what is possibly the biggest breach in the company’s history, with __ __and former employees having personally identifiable information (PII) leaked. over 75,000 employees According to the reports which dubbed the stolen data as “The Tesla Files”, the 100 GB leak included: Employees’ personal information like their names, addresses, phone numbers, and Social Security numbers Customer bank details Production secrets Customer complaints about Tesla’s Full Self-Driving (FSD) features Even CEO Elon Musk reportedly had his Social Security number leaked. We have since learned that there were two culprits behind the breach: who sent the massive data dump to the German newspaper Handelsblatt. two former Tesla employees The good news for Tesla is that the outlet has announced that they will not be publishing the contents of the leak. As a journalistic organization, especially in Germany, Handelsblatt could find itself in hot water for publishing peoples’ PII. However, the publisher did note the fact that the leaks included complaints about safety issues with the vehicles, which could still cause Tesla a fair amount of grief as writing about these issues could easily fall in the “public interest” bucket for journalists. The bad news is that the company may still be on the hook for in fines due to data privacy violations under GDPR. $3.3 billion While the leak is interesting in itself for reasons of scale and the high profile target, in this article, we are going to look at the role that fostering loyalty with employees can play in helping to tamper with the desire of employees to want to turn against their organizations. But first, let's take a look at Tesla’s history of dealing with insider threats. Tesla's History of Insider Threat Incidents This is not the first time that Tesla found itself in the headlines in terms of an insider threat incident. In fact, the company has a mixed bag when it comes to its history of insider threat incidents over the years. In 2018, Musk announced that a malicious employee had conducted " " to the company, exfiltrating large quantities of data and making changes to their systems. extensive and damaging sabotage The employee reportedly was angry over not receiving the promotion that they thought they thought was coming to them and decided to make their displeasure known through less than pleasant means. On the other side of the scale, in 2020, an employee alerted the company that he had been approached by an old acquaintance who tried to . Thanks to the employee’s warning, Tesla was able to thwart the attack, and the ransomware crew member was arrested. bribe him into helping carry out a ransomware attack In both cases, we see the potential damage a malicious insider can cause to their organization and frankly how easy it can be for them to be successful. Why are Insider Threats so Difficult to Stop? Insider threats are one of the hardest for organizations to defend against because they are legitimate users who are already inside your systems. Unlike outsiders who need to steal/phish/buy credentials and then defeat MFA, an insider is a real user inside your organization who can act totally normally until the moment that they do not.\ This means no indicators of compromise and no leaked credentials showing up on “Have I been Pwned.” You often will not detect it until the damage has been done. An additional factor is the fact that many insider threats are not actually malicious. While they cause real harm with their leaks, they act unintentionally through mistakes and general negligence. According to the Verizon Data Breach Investigations Report for 2023, was the cause of 98% of errors that led to a data breach. carelessness This difficulty in detection has led many organizations to look for ways to head off incidents before they happen, looking at strategies for prevention. This is where the question of motivation and loyalty comes into play. Negative Impactors on Employee Loyalty Year after year, the number one motivator for insider threat incidents is financial gain. You know, good old-fashioned greed. And yet, the seed that leads to the betrayal is planted long before an employee decides to take the more drastic steps of stealing or harming your data. Looking at the , researchers found that 89% of privilege misuse cases were financially motivated, followed by a “grudge” at 13%. Verizon Data Breach Investigations Report for 2023 So, what leads to that grudge? According to Jacques Y. Kassa’s 2021 thesis “ at the Naval Postgraduate School in Monterey, CA, depression, hopelessness, frustration, disgruntlement, and anger are all emotional factors that play into insider threats. ” Modeling the Relationship Between Loyalty and Insider Threat Not a groundbreaking insight but we also know that organizations do not invest enough effort in checking in with employees to see how they are feeling about their place in the organization. Kassa writes that motivators like ideology, vengeance, retribution, espionage, and activism are more frequent in government organizations than in the civilian sectors. Perhaps in the case of Tesla, there may have been a desire to share some of the safety data with the public, though there is a question of whether this is activism or retribution. There are, however, a couple of factors to watch out for. Underpaid and Underappreciated Being well compensated for your work is the top motivator for basically every employee. Better than extra snacks or company activities, employees need to feel that they are appreciated for their work, and commensurate compensation is a clear indicator of that recognition and respect. Feeling Part of the Team During the pandemic, one of the struggles faced by organizations was in employees maintaining a sense of being on the team. Going remote meant not strengthening the in-person, informal bonds that can make a person feel comradery with their co-workers and deter them from wanting to cause them harm. After all, if these are just people that you see on Zoom calls and email chains, what do you owe them if a better opportunity comes along? This has primarily been a problem for retention, but it can also impact motivations for someone turning malicious. Mass Layoffs and the Great Resignation We are at a cultural moment that is frankly different from where we were five or ten years ago. Uncertainty in the job market is a constant, with organizations laying off large quantities of workers in massive waves. Big players like Amazon have cut workers in the past year, while Microsoft has laid off over people. 27,000 10,000 This has led to the justified feeling among many that their companies are not loyal to them. So why should they show loyalty back? Why not take some customer data or valuable IP before you get shown the door if it might give you a leg up at your next job? Combine this uncertainty with the Great Resignation that saw workers leave jobs that they may have put up with for something better. Maybe they found a better location, something totally remote, or having other good conditions that they decided to seek greener pastures elsewhere. Many simply looked around and saw that plenty of others were leaving their jobs, so why not them, too? 3 Tips for Reducing Insider Threat Risk Given the ways that employee loyalty can be diminished, how can organizations improve the positive feelings that their employees have and reduce their risk of an insider threat incident? Here are a few suggestions. Provide an Outlet for Frustration or Concerns Beyond the need to feel appreciated, people need to feel that they have a place to turn to when something is bothering them at work. A question any company, especially a manufacturer, should ask is whether employees have a place to go where they can voice ethical concerns in-house and see that they are handled with due seriousness. If workers do not have an internal channel for dealing with concerns, then they may seek options elsewhere. Invest in Education and Training There’s an interesting question about when it comes to accidental insider-caused incidents. On the one hand, they do not actively choose to harm their employer since the case is unintentional. However, on the other hand, a lack of interest in following the guidelines due to a lack of a strong commitment to their organization probably plays a role in these incidents occurring. Dealing with the non-malicious actors requires fewer sticks and more carrots to get results. Training and education can play a critical role here in preventing incidents. The advantages here can be two-fold. Learning the proper protocols for handling sensitive data and systems teaches them how to do the job correctly and safely. It also gives them a sense of ownership. When your organization invests time and resources into training them how to be better at their job and protect the organization, then chances increase that they will try to implement what they learn in the courses. Encourage but Verify with User Behavioral Analytics Even as we work to increase the level of trust with employees, we need to implement measures to verify that folks are on their best behavior. This means putting in place User Behavioral Analytics tools for continuously monitoring behavior in order to establish a baseline of activity. Once we understand how people normally interact with the systems that they are entitled to work with and think about which applications, data sets, etc they interact with regularly, then we can detect when they begin to act anomalously. Most insider threat cases follow similar patterns in that they have threat actors abuse their privileges and find ways to exfiltrate their pilfered data. By monitoring sensitive files, we can see who is accessing them and potentially who is stepping outside the lines and needs to be followed up with. Loyalty is Earned, Not a Given One note for clarification. Companies are not families. They hire, fire, downsize, and work in their own self-interest. Most of us do not fire our family members, even if we want to sometimes. Employees have the same right to leave an organization if they feel that their own needs are not being met. Having loyalty to a company does not mean that someone should stay if they are not happy. What it does mean is that if the organization acts properly, then it will engender enough goodwill to keep employees on the ethical and legal path of not stealing their data or seeking to cause them harm. And that may count for enough to help diffuse and prevent a potential incident.

Data Breach: What Tesla’s Biggest Insider Threat in 2023 Can Teach Us Going Into 2024

ডেটা লঙ্ঘন: 2023 সালে টেসলার সবচেয়ে বড় অভ্যন্তরীণ হুমকি কী আমাদের 2024-এ যেতে শেখাতে পারে

Datenschutzverletzung: Was uns Teslas größte Insider-Bedrohung im Jahr 2023 für das Jahr 2024 lehren kann

Violación de datos: lo que la mayor amenaza interna de Tesla en 2023 puede enseñarnos de cara a 2024

Violation de données : ce que la plus grande menace interne de Tesla en 2023 peut nous apprendre à l’approche de 2024

डेटा उल्लंघन: 2023 में टेस्ला का सबसे बड़ा अंदरूनी खतरा हमें 2024 में क्या सिखा सकता है

データ侵害: 2023 年のテスラ最大のインサイダー脅威が 2024 年に向けて私たちに教えてくれること

데이터 침해: 2023년 Tesla의 가장 큰 내부자 위협이 2024년에 우리에게 가르쳐 줄 수 있는 것

Violação de dados: o que a maior ameaça interna da Tesla em 2023 pode nos ensinar em 2024

Утечка данных: чему нас может научить крупнейшая инсайдерская угроза Tesla в 2023 году в 2024 году

Veri İhlali: Tesla'nın 2023'teki En Büyük İçeriden Tehdidi, 2024'e Girerken Bize Ne Öğretebilir?

Vi phạm dữ liệu: Mối đe dọa nội bộ lớn nhất của Tesla vào năm 2023 có thể dạy chúng ta điều gì khi bước sang năm 2024

数据泄露：进入 2024 年，特斯拉 2023 年最大的内部威胁可以告诉我们什么

The Fugitive.

The American Connection.

Loneliness and the Biography of a Living Person

SAN FRANCISCO, CALIFORNIA - HackerNoon, the technology publishing company, is pleased to announce the nominees for its annual “Startups of the Year” award, featuring the most promising and innovative companies in the San Francisco startup ecosystem. The list, compiled by the HackerNoon team, showcases over 800 startups that are making significant contributions to the San Francisco economy and driving innovation in their respective industries. Nominations for the Startups of the Year 2023 are open! Nominate your company as the . startup of the year in San Fransisco here https://www.youtube.com/watch?v=ZqIW54yMsNc&t=2s&embedable=true Among the nominees in San Francisco for Startups of the Year 2023 are: PoplarML Nodle Cryenx Labs Willow Servicing Retain My Team Remio BabylonAI Fungify Monterey Openlayer Incoggo Polymath Feathery RunWhen Recruiterflow Beacon AI Booth.ai General Micro Technology Enterpret Lemonflow Pixly.tv Hyperstack HireQuotient Found Aviator Finch Dispatch Content Beta Zenpath ScreenMeet All of these startups have demonstrated remarkable growth and innovation in their fields. “We are thrilled to recognize the top startups in San Francisco and celebrate their achievements,” said . “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” David Smooke, HackerNoon Founder & CEO is a community-driven voting platform that recognizes startups that are changing technology and the world for the better. Starting May 8th, 2023, the internet can nominate and vote for the best ventures in their city. The voting period will remain open until December 31, 2023, and winners will be announced in January 2024 after a comprehensive evaluation by the HackerNoon team. Startups of the Year 2023 In its , a total of 44,427 startups were nominated in 16 different regions, and the community cast 234,344 votes during a 6-month long campaign in favor of budding companies that were growing and trending across every major city in the world. The received perks from sponsors, like , and a from HackerNoon. previous iteration 1,382 winners 3,000 Brex Points tech company news page As a proponent of the open data movement, HackerNoon releases the unique social data gathered from Startups of the Year on GitHub as “ ” for valuable insights. /where-startups-trend Apart from validation from the world's greenest tech publication, all nominees relevant to their domain, region, and funding. They will also receive an and a free version of their very own on HackerNoon. get free interviews amazing discount package designed for their startup Tech Company News Page These pages include business overviews (such as head count, founding year, bio, social links, and domain ranking) coupled with dynamic updates every time the company is mentioned in HackerNoon stories or relevant headlines around the web. People can , and vote once per day per award for who they think should be Startup of the Year all the way up to December 31st. nominate their favorite startups until June 8th In addition to the award itself, winners receive exclusive interviews, an exclusive NFT from HackerNoon, and a for life. .Tech Domain For more information about the “Startups of the Year 2023” list and the nominated companies, please visit our . FAQ page About HackerNoon HackerNoon is a technology publication that aims to empower developers, entrepreneurs, and tech enthusiasts to share their knowledge and expertise with a global audience. With a readership of over 4M+ monthly visitors, HackerNoon provides a platform for startups and innovators to showcase their work and connect with a vibrant community of like-minded individuals. Companies that blog get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Startups Package Startups Of The Year 2023 is sponsored by . .Tech Domains Nominations for the Startups of the Year 2023 are open! Nominate your company as the . startup of the year in San Fransisco here

Startups of The Year 2023: 800+ Startups Nominated in San Francisco

Since the three digital leaders, Apple, Google, and Microsoft, are pushing passkeys as the new login standard, the current technical support for passkeys is already very high. In this article, we will share our insights on the current data we gained over the past six months. Passkeys are the new standard for authentication. Passkeys are the latest technological advancement in login standards: Due to their technical concept, they are very secure by design and provide the best customer experience currently possible in logins. The three big platform and operating system leaders, Apple, Google, and Microsoft, enforce passkeys as the new standard. What does “passkey-ready” mean? (e.g., via facial recognition, fingerprint scan, or PIN pattern). In general, there is a further distinction between passkeys that are available on a single device and passkeys that are automatically synced in a cloud account (e.g., via Apple iCloud KeyChain, Google Password Manager, or Microsoft account). In the following, we include both types of passkeys for the analysis. Passkey-ready means a device can create and use passkeys for authentication The current technical support in practice is already very high, with over 80% of all devices supporting passkeys (see data below). All three major platforms have announced their full support meaning passkeys will be available on all new devices and thus will be available on most devices soon. Apple’s Passkey Push: Synced passkeys: available on iPhones with iOS 16+, iPads with iPadOS 16+, and MacBooks with macOS Ventura, Monterey, and Big Sur Non-synced passkeys: available for iPhones with iOS 15+, iPads with iPadOS 15+, and MacBooks with macOS Catalina+ Google Passkey Push: Synced passkeys: available on Android devices with Android 9+ Non-synced passkeys: available on Android devices with Android 7+ Microsoft Passkey Push: Synced passkeys: available soon with a new Windows Hello update Non-synced passkeys: available for devices with Windows 10 (activated Windows Hello) and Windows 11 In 2023, >80% are already “passkey-ready.” To validate if the vast majority of your customers are "passkey-ready,” Corbado built a free tool that analyzes how many of your users are technically ready to use passkeys. Thereby, the following data is gathered and used for the analysis: support: the device and browser combination’s support for WebAuthn, the underlying technology of passkeys WebAuthn support: the existence of a device’s built-in authenticator (e.g., Face ID, Touch ID, or Windows Hello) Platform auth support: the device and browser combination’s support for Conditional UI allows to automatically fill in passkey, including the identifier in login forms, which is another significant advancement in authentication Conditional UI ‍ In our , , and , users have been able to test passkeys over the past six months. We have evaluated anonymous, technical data regarding their passkey readiness. Overall, the number of visitors was a medium-sized four-digit number in the analyzed months. demo developer panel website Based on that, the following insights were gained: Almost of devices and browsers . This was expected as mainly digital users with modern devices and browsers visited our services. Checking the raw data, there was just a tiny fraction (less than 0.1%) using an outdated browser or device. 100% support WebAuthn is available on 80% of the devices, and the trend is increasing. This was very interesting to see that most devices already have the hardware to use passkeys (e.g., facial recognition, fingerprint scanner, or PIN pattern). This also proves that a lot of desktop devices already support passkeys. We expect this number to increase as old devices are replaced over time. Platform auth support of device and browser combinations . From the data, it is evident that the support increased drastically when Chrome and Safari announced their support and made it available for all users (before, it was only available for developers, which explains the low support). Over 70% support Conditional UI To sum these findings up: most (>80%) user devices are passkey-ready today (based on the visitors from our sites). We keep analyzing the data and expect platform auth and Conditional UI support to increase steadily over time. Determine your users’ passkey-readiness with Corbado’s free passkeys analyzer. To prove that the vast majority of users are "passkey-ready," we have developed a free tool that analyzes how many of your users’ devices are technically able to use passkeys. As passkeys are the new login standard on the web, everyone offering some form of login should consider integration into websites and apps soon. If you are unsure how many of your users are passkey-ready, you can find out with our free passkey analyzer. It’s riskless and has no lock-in. With two lines of code added to your website or app, you can start analyzing. The passkeys analyzer is fully GDPR-compliant, anonymous, and uses no cookies. It can be integrated in less than five minutes and has no impact on the user experience. <script src="https://analyze.corbado.com/assets/bundle.js"></script>

<script>tracking.visit(window, PublicKeyCredential, pro-xxx)</script> Start analyzing now and then offer passkey logins to these users. Trust us: once they experience passkey, they will use and demand them. Besides insights regarding passkey readiness, the passkey analyzer also provides data regarding total visitors and used browsers and operating systems. Use our and find out your users’ passkey-readiness! passkey analyzer

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